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Commit 3fc4e072 authored by Lucas Pina's avatar Lucas Pina :skull_crossbones:
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Upload fortis_unity_ws setup

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.gitignore 0 → 100644
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.catkin_tools
.vscode
build
logs
devel
src/*
README.md
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# fortis_unity_ws
Development workspace for fortis project with unity sample
\ No newline at end of file
Development workspace for fortis project with unity sample
# Requirements:
## install:
#### install GTSAM lib for LIO-SAM
`sudo apt-get install -y software-properties-common`
`sudo add-apt-repository ppa:borglab/gtsam-release-4.0`
```bash
sudo apt install -y \
libgtsam-dev \
libgtsam-unstable-dev
```
#### install Move Base Flex dependency
`sudo apt install ros-noetic-costmap-2d`
`sudo apt install ros-noetic-nav-core`
`sudo apt install ros-noetic-move-base-flex`
#### install Open3D
# Execute:
`./tmux/execute_sim.sh`
#### PIP install
tsp_solver2
frames.gv 0 → 100644
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digraph G {
"base_link" -> "manitou_imu"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 8.163 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"map" -> "base_link"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "top_left_lidar"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "top_rigth_lidar"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "top_center_lidar"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "camera_frame"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "wheel_BL"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "wheel_BR"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "wheel_FR"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"base_link" -> "wheel_FL"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
"map" -> "odom"[label="Broadcaster: /unity_endpoint_lucas_Victus_40937_4809900339950455392\nAverage rate: 10.204 Hz\nMost recent transform: 109.720 ( 0.080 sec old)\nBuffer length: 4.900 sec\n"];
edge [style=invis];
subgraph cluster_legend { style=bold; color=black; label ="view_frames Result";
"Recorded at time: 109.800"[ shape=plaintext ] ;
}->"map";
}
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ros_config/.gitkeep 0 → 100644
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ros_config/dm_run_behavior/fortis_fsm.yaml 0 → 100644
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behavior_name: fortis_fsm
args:
# Init wait time
waiting_time: 1
ros_config/dm_run_behavior/fortis_load_pallet.yaml 0 → 100644
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behavior_name: fortis_load_pallet
args:
# Init wait time
waiting_time: 2
n_iterations: 4
base_frame: "base_link"
offset_front_pallet: 2.5
ros_config/dm_run_behavior/fortis_nav.yaml 0 → 100644
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behavior_name: fortis_nav
args:
# Init wait time
waiting_time: 2
# # Waypoint navigation action node
# waypoint_navigation_topic: waypoint_navigation
# waypoint_controller: ""
# waypoint_planner: ""
# waypoint_recovery_behaviors: ""
waypoint_start_index: 10
# # Database access
# db_dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
# db_storage: storage
# # Use geographic coordinates as waypoints?
ll_coords_data: 0
# # (ll_coords_data == false)
# db_path_collection: Paths
# # (ll_coords_data == true)
# db_ll_coord_collection: GeoCoordinates
# ll_conv_srv_topic: fromLL
# ll_coords_map_frame: map
# # set datum before executing?
# # This is used to make sure that the dispatched coordinates are relative to the correct
# # geographic reference (saved during the recording of cartesian coordinates).
# set_datum: 0
# set_datum_srv_topic: set_datum
inputs:
waypoints_id: wpn_001 #""
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ros_config/dm_run_behavior/fortis_task.yaml 0 → 100644
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behavior_name: fortis_task
args:
# Init wait time
waiting_time: 10
ros_config/dm_run_behavior/fortis_unload_pallet.yaml 0 → 100644
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behavior_name: fortis_unload_pallet
args:
base_frame: "base_link"
map_frame: "map"
ros_config/dm_run_behavior/record_cartesian_coords.yaml 0 → 100644
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behavior_name: record_cartesian_coords
args:
# Capture info
coord_topic: odom
num_of_coords: 4
min_allowed_trust: 0.0
tf_poses: 1
tf_poses_frame: map
# Database access
dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
storage: storage
path_collection: Paths
create_if_empty: 1
overwrite_if_exists: 1
inputs:
path_id: wpn_001 #""
ros_config/dm_run_behavior/record_discrete_cartesian_coords.yaml 0 → 100644
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behavior_name: record_discrete_cartesian_coords
args:
# Capture info
coord_topic: /waypoints #/odom #/lio_sam/mapping/odometry #ekf/local_odom #ekf/global_odom
num_of_coords: 40
min_allowed_trust: 0.0
tf_poses: 1
tf_poses_frame: map
# Database access
dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
storage: storage
path_collection: Paths
create_if_empty: 1
overwrite_if_exists: 1
inputs:
path_id: wpn_001 #""
ros_config/dm_run_behavior/record_geographic_coords.yaml 0 → 100644
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behavior_name: record_geographic_coords
args:
# Capture info
navsatfix_topic: /gps/fix
num_of_coords: 5 #1
min_allowed_trust: 0.9
# Database access
dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
storage: storage
ll_coords_collection: GeoCoordinates
create_if_empty: 1
overwrite_if_exists: 0
inputs:
ll_coords_id: rrt_exploration_vertices #""
ros_config/dm_run_behavior/waypoint_navigation_procedure.yaml 0 → 100644
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behavior_name: waypoint_navigation_procedure
args:
# Waypoint navigation action node
waypoint_navigation_topic: waypoint_navigation
waypoint_controller: ""
waypoint_planner: ""
waypoint_recovery_behaviors: ""
waypoint_start_index: 0
# Database access
db_dsn: postgresql://root@127.0.0.1:26257/nakama?sslmode=disable
db_storage: storage
# Use geographic coordinates as waypoints?
ll_coords_data: 0
# (ll_coords_data == false)
db_path_collection: Paths
# (ll_coords_data == true)
db_ll_coord_collection: GeoCoordinates
ll_conv_srv_topic: fromLL
ll_coords_map_frame: map
# set datum before executing?
# This is used to make sure that the dispatched coordinates are relative to the correct
# geographic reference (saved during the recording of cartesian coordinates).
set_datum: 0
set_datum_srv_topic: set_datum
inputs:
waypoints_id: wpn_001 #""
ros_config/loc_lio_sam.yaml 0 → 100644
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lio_sam:
# Topics
pointCloudTopic: "front_lslidar_point_cloud_filtered" # "back_lslidar_point_cloud_filtered" # "front_lslidar_point_cloud_filtered" "fused_point_cloud" # Point cloud data
imuTopic: "imu/wit/imu" # IMU data
odomTopic: "odom" # IMU pre-preintegration odometry, same frequency as IMU
gpsTopic: "odometry/gps" # GPS odometry topic from navsat, see module_navsat.launch file
# Topics - Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
gpsFixTopic: "/loader/ugv1/gps/fix" # GPS navsatfix topic from sensor, used to determine wether to apply GPS factor or not
gpsFixFilteredTopic: "" # Its subscribed but not used for anything yet
# Frames
lidarFrame: "base_link"
baselinkFrame: "base_link"
odometryFrame: "odom"
mapFrame: "map"
# Frames - Ingeniarius
mapFrameAsChild: false # Pass the map_frame as child of base_link, following the CTU MRS approach (https://ctu-mrs.github.io/docs/system/frames_of_reference.html)
# Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
liosamMode: 0 # LIO-SAM Mode 0: SLAM, Mode 1: Localization, Mode 2: Application
gpsInitialLocalization: true
gpsUpatedDistFix: 2.0 # Min update distance for liosam update is mapframe went it have GPSfix, status = 2
# GPS Settings
useImuHeadingInitialization: true # if using GPS data, set to "true"
useGpsElevation: true # if GPS elevation is bad, set to "false"
gpsCovThreshold: 2.0 # m^2, threshold for using GPS data
poseCovThreshold: 25.0 # m^2, threshold for using GPS data
# Export settings
savePCD: false # https://github.com/TixiaoShan/LIO-SAM/issues/3
savePCDDirectory: "/scliosam/data/" # use global path, and end with "/". Warning: if you have already data in the above savePCDDirectory, it will all remove and remake them. Thus, backup is recommended if pre-made data exist.
# Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
eraseLOAMFolder: false
saveRawCloud: false
graphTest: false # Save factor graph drawing
saveGTSAM: false # Save graph
saveGTSAMDirectory: "gtsam/" # Directory of gtsam
# Analysis Localization
buildConfusionMatrix: false # Build a confusion matrix
savePCDDirectory_Q: "/scliosam/" # Directory to save the matrix
# Quality of localization
covarianceMedium: 1.0
covarianceHigh: 2.0
# Sensor Settings
sensor: velodyne # lidar sensor type, 'velodyne' or 'ouster' or 'livox' or 'leishen'
N_SCAN: 16 # number of lidar channel (i.e., 16, 32, 64, 128)
Horizon_SCAN: 2000 # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
downsampleRate: 1 # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1
lidarMinRange: 1.0 # default: 1.0, minimum lidar range to be used
lidarMaxRange: 1000.0 # default: 1000.0, maximum lidar range to be used
# IMU Settings
imuAccNoise: 3.9939570888238808e-03
imuGyrNoise: 1.5636343949698187e-03
imuAccBiasN: 6.4356659353532566e-05
imuGyrBiasN: 3.5640318696367613e-05
imuGravity: 9.80511
imuRPYWeight: 0.01
# Extrinsics (lidar -> IMU)
extrinsicTrans: [0.0, 0.0, 0.0]
extrinsicRot: [1, 0, 0, 0, 1, 0, 0, 0, 1]
extrinsicRPY: [1, 0, 0, 0, 1, 0, 0, 0, 1]
# LOAM feature threshold
edgeThreshold: 1.0
surfThreshold: 0.1
edgeFeatureMinValidNum: 10
surfFeatureMinValidNum: 100
# voxel filter paprams
odometrySurfLeafSize: 0.2 # default: 0.4 - outdoor, 0.2 - indoor
mappingCornerLeafSize: 0.1 # default: 0.2 - outdoor, 0.1 - indoor
mappingSurfLeafSize: 0.2 # default: 0.4 - outdoor, 0.2 - indoor
# robot motion constraint (in case you are using a 2D robot)
z_tollerance: 1000 # meters
rotation_tollerance: 1000 # radians
# CPU Params
numberOfCores: 4 # number of cores for mapping optimization
mappingProcessInterval: 0.15 # seconds, regulate mapping frequency
# Surrounding map
surroundingkeyframeAddingDistThreshold: 1.0 # meters, regulate keyframe adding threshold
surroundingkeyframeAddingAngleThreshold: 0.2 # radians, regulate keyframe adding threshold
surroundingKeyframeDensity: 2.0 # meters, downsample surrounding keyframe poses
surroundingKeyframeSearchRadius: 50.0 # meters, within n meters scan-to-map optimization (when loop closure disabled)
# Loop closure
loopClosureEnableFlag: true
loopClosureFrequency: 1.0 # Hz, regulate loop closure constraint add frequency
surroundingKeyframeSize: 50 # submap size (when loop closure enabled)
historyKeyframeSearchRadius: 15.0 # meters, key frame that is within n meters from current pose will be considerd for loop closure
historyKeyframeSearchTimeDiff: 30.0 # seconds, key frame that is n seconds older will be considered for loop closure
historyKeyframeSearchNum: 25 # number of hostory key frames will be fused into a submap for loop closure
historyKeyframeFitnessScore: 0.3 # icp threshold, the smaller the better alignment
# Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
performRSLoopClosureFlag: true # Perform RS Loop Closure
performSCLoopClosureFlag: false # Perform SC Loop Closure
# Visualization
globalMapVisualizationSearchRadius: 1000.0 # meters, global map visualization radius
globalMapVisualizationPoseDensity: 1.0 #10.0 # meters, global map visualization keyframe density
globalMapVisualizationLeafSize: 0.4 #0.20 # meters, global map visualization cloud density
# Ingeniarius' version at: https://gitlab.ingeniarius.pt/ingeniarius_common/perception/lio-sam.git
# ICP Localization
icpLocalizationStep: 1 # Step
icpLocalizationAscendingOrder: false # Ascending order
icpLocalizationPreliminary: false # ICP Preliminary Localization
# PCD Localization
pcdInitialLocalization: false # Initial localization using PCD
pcdLocalizationThreshold: 0.0
# SCD Localization
scdInitialLocalization: true # Initial localization using SCD
scdPCNumRing: 20
scdPCNumSector: 60
scdPCMaxRadius: 80.0
scdInput: single_scan # single_scan single_feature multi_scan
numberGpsOdometryMessageToInitialize: 30 # minimum number of navsat's odometry messages in order to initialize the GPS factor
# Relocalization
relocalizeMode: 3 # 0 - Rviz, 1 - trajectory, 2 - searching
relocalizeThreadFrequency: 1.0 # the frequency of the relocalization thread logic
relocalizeGlobalTimeout: 10.0 # global relocalization timeout. The period at which a better localization for the odom frame is looked.
relocalizeHistoryKeyframeFitnessScore: 1.0 # the fitness score threshold for relocalization.
setMaxCorrespondenceDistance:
100 # Set the maximum distance threshold between two correspondent points in source <-> target
# If the distance is larger than this threshold, the points will be ignored in the alignment process
setMaximumIterations: 100 # Set the maximum number of iterations the internal optimization should run for
setTransformationEpsilon: 0.000001 # The maximum difference between two consecutive transformations in order to consider convergence
setEuclideanFitnessEpsilon:
0.000001 # Set the maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the
# algorithm is considered to have converged. The error is estimated as the sum of the differences
# between correspondences in an Euclidean sense, divided by the number of correspondences;
setRANSACIterations: 0 # Set the number of iterations RANSAC should run for.
numberOfAttempts: 10
useICPscanMatchGlobal: false
initMapGPS: false
numFramesEKF: 10
# Navsat (convert GPS coordinates to Cartesian)
navsat:
frequency: 50
wait_for_datum: true
delay: 0.0
magnetic_declination_radians: 0
yaw_offset: 0
zero_altitude: true
altitude: 0.0
broadcast_utm_transform: false
broadcast_utm_transform_as_parent_frame: false
publish_filtered_gps: true
# EKF for Navsat
ekf_gps:
publish_tf: false
map_frame: map
odom_frame: odom
base_link_frame: base_link
world_frame: map
frequency: 50
two_d_mode: false
sensor_timeout: 0.01
# -------------------------------------
# External IMU:
# -------------------------------------
imu0: "imu/wit/imu"
# make sure the input is aligned with ROS REP105. "imu_correct" is manually transformed by myself. EKF can also transform the data using tf between your imu and base_link
imu0_config:
[
false,
false,
false,
true,
true,
true,
false,
false,
false,
false,
false,
true,
true,
true,
true,
]
imu0_differential: false
imu0_queue_size: 50
imu0_remove_gravitational_acceleration: true
# -------------------------------------
# Odometry (From Navsat):
# -------------------------------------
odom0: odometry/gps
odom0_config:
[
true,
true,
true,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
false,
]
odom0_differential: false
odom0_queue_size: 10
# x y z r p y x_dot y_dot z_dot r_dot p_dot y_dot x_ddot y_ddot z_ddot
process_noise_covariance:
[
1.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
10.0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.03,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.03,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.25,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.25,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.04,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.01,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.01,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.5,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.01,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.01,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.015,
]
# initial_estimate_covariance: [1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1, 0,
# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-1]
ros_config/nav_move_base_flex/common_costmap_params.yaml 0 → 100644
View file @ 3fc4e072
# footprint: [[0.94, -0.84], [0.94, 0.84], [-1.66, 0.84], [-1.66, -0.84]] # without tool
# footprint: [[1.59, -0.84], [1.59, 0.84], [-1.66, 0.84], [-1.66, -0.84]] # with fork
footprint: [[2.7, -0.84], [2.7, 0.84], [-1.66, 0.84], [-1.66, -0.84]] # with tool 2.30
footprint_padding: 0.0
make_plan_clear_costmap: false # parameters of make_plan service
make_plan_add_unreachable_goal: false # true, parameters of make_plan service
global_static_layer:
enabled: true # Whether to apply this plugin or not
first_map_only: false # Only subscribe to the first message on the map topic, ignoring all subsequent messages
unknown_cost_value: -1 # The value for which a cost should be considered unknown when reading in a map from the map server. If the costmap is not tracking unknown space, costs of this value will be considered occupied. A value of zero also results in this parameter being unused.
lethal_cost_threshold: 100 # The threshold value at which to consider a cost lethal when reading in a map from the map server.
subscribe_to_updates: true # In addition to map_topic, also subscribe to map_topic + "_updates"
track_unknown_space: true # If true, unknown values in the map messages are translated directly to the layer. Otherwise, unknown values in the map message are translated as FREE_SPACE in the layer.
use_maximum: false # Only matters if the static layer is not the bottom layer. If true, only the maximum value will be written to the master costmap
trinary_costmap: true # If true, translates all map message values to NO_INFORMATION/FREE_SPACE/LETHAL_OBSTACLE (three values). If false, a full spectrum of intermediate values is possible.
map_topic: /loader/ugv1/lio_sam/mapping/map_global/map #The topic that the costmap subscribes to for the static map. This parameter is useful when you have multiple costmap instances within a single node that you want to use different static maps.
global_inflation_layer:
enabled: true # Whether to apply this plugin or not
cost_scaling_factor: 3.0 # exponential rate at which the obstacle cost drops off (default: 10)
inflation_radius: 3.0 # 1.0m out of 1.6m of robot's width (the robot is actually wider)
global_obstacle_layer_0:
enabled: true
voxel_decay: 10 # seconds if linear, e^n if exponential
decay_model: 0 # 0=linear, 1=exponential, -1=persistent
voxel_size: 0.05 # meters
track_unknown_space: true # default space is known
mark_threshold: 0 # voxel height
update_footprint_enabled: true
combination_method: 1 # 1=max, 0=override
origin_z: 0.0 # meters
publish_voxel_map: true # default off
transform_tolerance: 0.6 # seconds
mapping_mode: false # default off, saves map not for navigation
map_save_duration: 60 # default 60s, how often to autosave
observation_sources: pc1_mark pc1_clear
pc1_mark:
data_type: PointCloud2
topic: /loader/ugv1/lio_sam/deskew/cloud_deskewed
marking: true
clearing: false
obstacle_range: 3.0 # meters
min_obstacle_height: 0.3 # default 0, meters
max_obstacle_height: 2.0 # default 3, meters
expected_update_rate: 0.0 # default 0, if not updating at this rate at least, remove from buffer
observation_persistence: 0.0 # default 0, use all measurements taken during now-value, 0=latest
inf_is_valid: false # default false, for laser scans
filter: "passthrough" # default passthrough, apply "voxel", "passthrough", or no filter to sensor data, recommend on
voxel_min_points: 0 # default 0, minimum points per voxel for voxel filter
clear_after_reading: true # default false, clear the buffer after the layer gets readings from it
pc1_clear:
data_type: PointCloud2
topic: /loader/ugv1/lio_sam/deskew/cloud_deskewed
marking: false
clearing: true
max_z: 7.0 # default 0, meters
min_z: 0.1 # default 10, meters (the back lidar has 4m blind spot)
vertical_fov_angle: 0.4 #523 # default 0.7, radians. For 3D lidars it's the symmetric FOV about the planar axis.
vertical_fov_padding: 0.05 # 3D Lidar only. Default 0, in meters
horizontal_fov_angle: 6.29 # 3D lidar scanners like the VLP16 have 360 deg horizontal FOV.
decay_acceleration: 1.0 # default 0, 1/s^2.
model_type: 1 # default 0, model type for frustum. 0=depth camera, 1=3d lidar like VLP16 or similar
global_obstacle_layer_1:
enabled: false
voxel_decay: 60 # seconds if linear, e^n if exponential
decay_model: 0 # 0=linear, 1=exponential, -1=persistent
voxel_size: 0.2 # meters
track_unknown_space: true # default space is known
max_obstacle_height: 2.5 # meters
unknown_threshold: 15 # voxel height
mark_threshold: 3 # voxel height
update_footprint_enabled: true
combination_method: 0 # 1=max, 0=override
obstacle_range: 5.0 # meters
origin_z: 0.0 # meters
publish_voxel_map: false # default off
transform_tolerance: 0.6 # seconds
mapping_mode: false # default off, saves map not for navigation
map_save_duration: 60 # default 60s, how often to autosave
observation_sources: pc2_mark pc2_clear
pc2_mark:
data_type: PointCloud2
topic: /traversability_map_visualization/traversability_cloud/passed
marking: true
clearing: false
min_obstacle_height: 0.0 # default 0, meters (initial: 0.05)
max_obstacle_height: 2.5 # default 3, meters
expected_update_rate: 0.0 # default 0, if not updating at this rate at least, remove from buffer
observation_persistence: 0.0 # default 0, use all measurements taken during now-value, 0=latest
inf_is_valid: false # default false, for laser scans
filter: "passthrough" # default passthrough, apply "voxel", "passthrough", or no filter to sensor data, recommend on
voxel_min_points: 0 # default 0, minimum points per voxel for voxel filter
clear_after_reading: true # default false, clear the buffer after the layer gets readings from it
pc2_clear:
enabled: true #default true, can be toggled on/off with associated service call
data_type: PointCloud2
topic: /traversability_map_visualization/traversability_cloud/passed
marking: false
clearing: true
max_z: 5.0 # default 0, meters
min_z: 0.0 # default 10, meters (the back lidar has 4m blind spot)
vertical_fov_angle: 0.523 # default 0.7, radians. For 3D lidars it's the symmetric FOV about the planar axis.
vertical_fov_padding: 0.05 # 3D Lidar only. Default 0, in meters
horizontal_fov_angle: 6.29 # 3D lidar scanners like the VLP16 have 360 deg horizontal FOV.
decay_acceleration: 5.0 # default 0, 1/s^2.
model_type: 1 # default 0, model type for frustum. 0=depth camera, 1=3d lidar like VLP16 or similar
local_static_layer_0:
enabled: true
first_map_only: false
unknown_cost_value: -1
lethal_cost_threshold: 100
subscribe_to_updates: true
track_unknown_space: true
use_maximum: false
trinary_costmap: true
map_topic: /loader/ugv1/lio_sam/mapping/map_global/map
local_static_layer_1:
enabled: true
first_map_only: false
unknown_cost_value: -1
lethal_cost_threshold: 100
subscribe_to_updates: false
track_unknown_space: true
use_maximum: false
trinary_costmap: true
map_topic: /traversability_map_visualization/traversability_map
local_inflation_layer:
enabled: true # Whether to apply this plugin or not
cost_scaling_factor: 10 # exponential rate at which the obstacle cost drops off (default: 10)
inflation_radius: 1.0 # max. distance from an obstacle at which costs are incurred for planning paths (min » costmap resolution)
local_obstacle_layer_0:
enabled: true
voxel_decay: 2 # seconds if linear, e^n if exponential
decay_model: 0 # 0=linear, 1=exponential, -1=persistent
voxel_size: 0.1 # meters
track_unknown_space: true # default space is known
max_obstacle_height: 5.0 # meters
unknown_threshold: 15 # voxel height
mark_threshold: 3 # voxel height
update_footprint_enabled: true
combination_method: 1 # 1=max, 0=override
obstacle_range: 10.0 # meters
origin_z: 0.0 # meters
publish_voxel_map: false # default off
transform_tolerance: 0.6 # seconds
mapping_mode: false # default off, saves map not for navigation
map_save_duration: 60 # default 60s, how often to autosave
observation_sources: pc1_mark pc1_clear
pc1_mark:
data_type: PointCloud2
topic: /loader/ugv1/lio_sam/deskew/cloud_deskewed
marking: true
clearing: false
min_obstacle_height: 0.2 # default 0, meters
max_obstacle_height: 2.0 # default 3, meters
expected_update_rate: 0.0 # default 0, if not updating at this rate at least, remove from buffer
observation_persistence: 0.0 # default 0, use all measurements taken during now-value, 0=latest
inf_is_valid: false # default false, for laser scans
filter: "passthrough" # default passthrough, apply "voxel", "passthrough", or no filter to sensor data, recommend on
voxel_min_points: 0 # default 0, minimum points per voxel for voxel filter
clear_after_reading: true # default false, clear the buffer after the layer gets readings from it
pc1_clear:
enabled: true #default true, can be toggled on/off with associated service call
data_type: PointCloud2
topic: /loader/ugv1/lio_sam/deskew/cloud_deskewed
marking: false
clearing: true
max_z: 10.0 # default 0, meters
min_z: 0.0 # default 10, meters (the back lidar has 4m blind spot)
vertical_fov_angle: 0.523 # default 0.7, radians. For 3D lidars it's the symmetric FOV about the planar axis.
vertical_fov_padding: 0.05 # 3D Lidar only. Default 0, in meters
horizontal_fov_angle: 6.29 # 3D lidar scanners like the VLP16 have 360 deg horizontal FOV.
decay_acceleration: 5.0 # default 0, 1/s^2.
model_type: 1 # default 0, model type for frustum. 0=depth camera, 1=3d lidar like VLP16 or similar
local_obstacle_layer_1:
enabled: false
voxel_decay: 15 # seconds if linear, e^n if exponential
decay_model: 0 # 0=linear, 1=exponential, -1=persistent
voxel_size: 0.2 # meters
track_unknown_space: true # default space is known
max_obstacle_height: 2.5 # meters
unknown_threshold: 15 # voxel height
mark_threshold: 3 # voxel height
update_footprint_enabled: true
combination_method: 0 # 1=max, 0=override
obstacle_range: 5.0 # meters
origin_z: 0.0 # meters
publish_voxel_map: false # default off
transform_tolerance: 0.6 # seconds
mapping_mode: false # default off, saves map not for navigation
map_save_duration: 60 # default 60s, how often to autosave
observation_sources: pc2_mark pc2_clear
pc2_mark:
data_type: PointCloud2
topic: /traversability_map_visualization/traversability_cloud/passed
marking: true
clearing: false
min_obstacle_height: 0.0 # default 0, meters (initial: 0.05)
max_obstacle_height: 2.5 # default 3, meters
expected_update_rate: 0.0 # default 0, if not updating at this rate at least, remove from buffer
observation_persistence: 0.0 # default 0, use all measurements taken during now-value, 0=latest
inf_is_valid: false # default false, for laser scans
filter: "passthrough" # default passthrough, apply "voxel", "passthrough", or no filter to sensor data, recommend on
voxel_min_points: 0 # default 0, minimum points per voxel for voxel filter
clear_after_reading: true # default false, clear the buffer after the layer gets readings from it
pc2_clear:
enabled: true #default true, can be toggled on/off with associated service call
data_type: PointCloud2
topic: /traversability_map_visualization/traversability_cloud/passed
marking: false
clearing: true
max_z: 5.0 # default 0, meters
min_z: 0.0 # default 10, meters (the back lidar has 4m blind spot)
vertical_fov_angle: 0.523 # default 0.7, radians. For 3D lidars it's the symmetric FOV about the planar axis.
vertical_fov_padding: 0.05 # 3D Lidar only. Default 0, in meters
horizontal_fov_angle: 6.29 # 3D lidar scanners like the VLP16 have 360 deg horizontal FOV.
decay_acceleration: 5.0 # default 0, 1/s^2.
model_type: 1 # default 0, model type for frustum. 0=depth camera, 1=3d lidar like VLP16 or similar
ros_config/nav_move_base_flex/controllers.yaml 0 → 100644
View file @ 3fc4e072
TebLocalPlannerROS:
# *************************************
# General Parameters
# *************************************
# Topic name of the odometry message
odom_topic: /loader/ugv1/odom
# Global planning frame
map_frame: map
# *************************************
# Robot Configuration Parameters
# *************************************
# Maximum translational velocity (m/s)
max_vel_x: 2.0 # NOT GPT # !!!
# Maximum absolute translational velocity of the robot while driving backwards (m/s)
max_vel_x_backwards: 0.1 # NOT GPT # # !!!
# Maximum strafing velocity of the robot (m/s)
# It should be zero for non-holonomic robots
max_vel_y: 0.0 # (default)
# Maximum translational velocity of the robot
# for omni robots, which is different from max_vel_x.
max_vel_trans: 0.0 # (default)
# Maximum angular velocity (rad/s)
max_vel_theta: 1.0 # NOT GPT #
# Maximum translational acceleration of the robot (m/s^2)
acc_lim_x: 1.0 # NOT GPT # 0.01 # !!!
# Maximum strafing acceleration of the robot (m/s^2)
# It should be zero for non-holonomic robots
acc_lim_y: 0.0 # (default)
# Maximum angular acceleration of the robot (rad/sec^2)
acc_lim_theta: 1.8 # 1.2 without tool
# Minimum turning radius of a carlike robot (set to zero for a diff-drive robot)
min_turning_radius: 0.0 # (default)
# The distance between the rear axle and the front axle
wheelbase: 1.0 # (default)
# Substitute the rotational velocity in the commanded
# velocity message by the corresponding steering angle [-pi/2,pi/2]
cmd_angle_instead_rotvel: false # (default)
# If true, updates the footprint before checking trajectory feasibility.
is_footprint_dynamic: false # (default)
# If true, use proportional saturation
use_proportional_saturation: false # (default)
# Model of the robot's footprint
# footprint_model:
# type: "line"
# line_start: [-0.930, 0.0] # [negative_length_component + width/2 + 0.05, 0.0]
# line_end: [0.450, 0.0] # [positive_length_component - width/2 - 0.05, 0.0]
# footprint_model:
# type: "line"
# line_start: [-0.930, 0.0] # [negative_length_component + width/2 + 0.05, 0.0]
# line_end: [1.150, 0.0] # [positive_length_component - width/2 - 0.05, 0.0]
footprint_model:
type: "line"
line_start: [-0.930, 0.0] # [negative_length_component + width/2 + 0.05, 0.0]
line_end: [2.7, 0.0] # [positive_length_component - width/2 - 0.05, 0.0]
# *************************************
# Goal Tolerance Parameters
# *************************************
# Allowed final euclidean distance to the goal position in meters
xy_goal_tolerance: 1.0
# Allowed final orientation error in radians
yaw_goal_tolerance: 3.14 # !!!
# Remove the goal velocity constraint such that the robot can
# arrive at the goal with maximum speed
free_goal_vel: false # (default)
# Below what maximum velocity we consider the robot to be stopped in translation
trans_stopped_vel: 0.01 # (default) # !!!
# Below what maximum rotation velocity we consider the robot to be stopped in rotation
theta_stopped_vel: 0.01 # (default) # !!!
# If true, complete the global plan
complete_global_plan: true # (default)
# *************************************
# Trajectory Configuration Parameters
# *************************************
# Enable automatic resizing of the trajectory w.r.t to the temporal resolution
teb_autosize: true # (default)
# Desired temporal resolution of the trajectory, It should be 1/control_rate.
dt_ref: 0.2 # GPT# 0.25 # !!!
# Hysteresis for automatic resizing depending on the current temporal resolution
# It should be 10% of the dt_ref
dt_hysteresis: 0.02 # GPT# 0.025 # !!!
# Minimum number of samples
min_samples: 3 # (default)
# Maximum number of samples; Warning: if too small the discretization/resolution
# might not be sufficient for the given robot model or obstacle avoidance does not
# work anymore.
max_samples: 500 # (default)
# Overwrite orientation of local subgoals provided by the global planner
global_plan_overwrite_orientation: true # (default)
# If true, underlying trajectories might be initialized with backwards motions
# in case the goal is behind the start within the local costmap
allow_init_with_backwards_motion: false
# The value determines the resolution of the reference path
# Min. separation between each two consecutive via-points extracted from the global plan
global_plan_viapoint_sep: 1.0 #3.0 #2.0 # !!!
# If true, the planner adheres to the order of via-points in the storage container
via_points_ordered: false # (default)
# Specify the maximum length (cumulative Euclidean distances) of the
# subset of the global plan taken into account for optimization
max_global_plan_lookahead_dist: 3.0 # GPT# 5.0 #20.0 # !!!
# Distance between robot and via_points of global plan which is used for pruning
global_plan_prune_distance: 1.0 # (default)
# If true, the planner uses the exact arc length in velocity, acceleration and
# turning rate computations (-> increased cpu time), otherwise the Euclidean
# approximation is used.
exact_arc_length: false # (default)
# Reinitialize the trajectory if a previous goal is updated with a
# separation of more than the specified value in meters (skip hot-starting)
force_reinit_new_goal_dist: 1.0 # (default)
# Reinitialize the trajectory if a previous goal is updated with an angular
# difference of more than the specified value in radians (skip hot-starting)
force_reinit_new_goal_angular: 0.5*M_PI # (default)
# Specify up to which pose on the predicted plan the
# feasibility should be checked each sampling interval
feasibility_check_no_poses: 5 # (default)
# Specify up to which distance from the robot the feasibility
# should be checked each sampling interval
# if -1, all poses up to feasibility_check_no_poses are checked.
feasibility_check_lookahead_distance: -1 # (default)
# Publish planner feedback containing the full trajectory and a list
# of active obstacles (should be enabled only for evaluation or debugging)
publish_feedback: true
# Minimum resolution collision check angular.
min_resolution_collision_check_angular: M_PI # (default)
# Min angular resolution used during the costmap collision
# check. If not respected, intermediate samples are added. [rad].
control_look_ahead_poses: 1 # (default)
# Index of the pose used to extract the velocity command.
prevent_look_ahead_poses_near_goal: 0 # (default)
# *************************************
# Obstacle Parameters
# *************************************
# Minimum desired separation from obstacles in meters
# (width / 2 + 0.05, padding is to avoid warning)
min_obstacle_dist: 0.90 # NOT GPT #
# Buffer zone arround obstacles with non-zero penalty costs
# It should be larger than min_obstacle_dist in order to take effect
inflation_dist: 1.4 # 1.0 without tool
# Buffer zone around predicted locations of dynamic obstacles
# with non-zero penalty costs
dynamic_obstacle_inflation_dist: 1.0
# If true, the motion of obstacles with non-zero velocity is predicted
# and considered during optimization via constant velocity model
include_dynamic_obstacles: true # (default)
# Specify if obstacles of the local costmap should be taken into account
include_costmap_obstacles: true # (default) # NOT GPT #
# Limit the occupied local costmap obstacles taken into account
# for planning behind the robot (m)
costmap_obstacles_behind_robot_dist: 1.5 # (default) # NOT GPT #
# Each obstacle position is attached to the closest pose on the
# trajectory in order to keep a distance
obstacle_poses_affected: 10 # (default) # NOT GPT #
# false -> actual strategy ( for each teb pose, find only "relevant" obstacles)
# true -> old strategy (for each obstacle, find nearest TEB pose)
legacy_obstacle_association: false # (default)
# All obstacles within a specifed distance are forced to be included
obstacle_association_force_inclusion_factor: 1.5 # (default)
# A multiple of all obstacles are ignored during optimization.
obstacle_association_cutoff_factor: 5 # (default)
# Define plugin name in order to convert costmap cells to points/lines/polygons
costmap_converter_plugin: "" # (default)
# If true, the costmap converter invokes its callback queue in diferent thread
costmap_converter_spin_thread: true # (default)
# Rate that defines how often the costmap_converter plugin
# processes the current costmap
costmap_converter_rate: 5.0 # (default)
# Ratio of the maximum velocities used as an upper bound when
# reducing the speed due to the proximity to a static obstacles.
obstacle_proximity_ratio_max_vel: 1 # (default)
# Distance to a static obstacle for which the velocity should be lower.
obstacle_proximity_lower_bound: 0 # (default)
# Distance to a static obstacle for which the velocity should be higher.
obstacle_proximity_upper_bound: 0.5 # (default)
# *************************************
# Optimization Parameters
# *************************************
# Number of actual solver iterations called in each outerloop interation
no_inner_iterations: 5 # (default) # NOT GPT #
# Each outerloop iteration automatically resizes the trajectory according
# to the desired temporal resolution dt_ref and invokes the internal optimizer
# (that performs no_inner_iterations)
no_outer_iterations: 4 # (default) # NOT GPT #
# Activate the optimization.
optimization_activate: true # (default) # NOT GPT #
# Print verbose information.
optimization_verbose: false # (default) # NOT GPT #
# Add a small safety margin to penalty functons for hard constraints approximations
penalty_epsilon: 0.001 # !!! # NOT GPT #
# Optimization weight for satisfynig the maximum allowed translational velocity
weight_max_vel_x: 2.0 # 1.0 # GPT #
# Optimization weight for satisfying the maximum allowed strafing velocity
# (in use only for holonomic robots)
weight_max_vel_y: 0.0
# Optimization weight for satisfynig the maximum allowed angular velocity
weight_max_vel_theta: 1.0 # (default) # NOT GPT #
# # Optimization weight for satisfynig the maximum allowed translational acceleration
weight_acc_lim_x: 1.0 # 0.0 # GPT #
# Optimization weight for satisfying the maximum allowed strafing acceleration
# (in use only for holonomic robots)
weight_acc_lim_y: 0.0
# Optimization weight for satisfynig the maximum allowed angular acceleration
weight_acc_lim_theta: 1.0 # 0.0 # GPT #
# Optimization weight for satisfying the non-holonomic kinematics
weight_kinematics_nh: 100.0 # NOT GPT #
# Optimization weight for forcing the robot to choose only forward directions
# A small weight (e.g. 1.0) still allows driving backwards
weight_kinematics_forward_drive: 500.0 # 0.001 # !!! # GPT #
# Optimization weight for enforcing a minimum turning radius
weight_kinematics_turning_radius: 1.0 # 0.0 # GPT #
# Optimization weight for contracting the trajectory w.r.t transition/execution time
weight_optimaltime: 1.0 # (default) # GPT #
# Optimization weight for contracting the trajectory w.r.t. path length.
weight_shortest_path: 0.0 # (default) # NOT GPT #
# Optimization weight for keeping a minimum distance from obstacles
weight_obstacle: 50.0 # (default) # NOT GPT #
# Optimization weight for the inflation penalty (should be small)
weight_inflation: 0.1 # (default)
# Optimization weight for keeping a minimum distance from obstacles
weight_dynamic_obstacle: 10.0 # 50.0 # (default) # GPT #
# Optimization weight for the inflation penalty of dynamic obstacles (should be small)
weight_dynamic_obstacle_inflation: 0.2 # 0.1 # (default) # GPT #
# Optimization weight for satisfying a maximum allowed velocity with
# respect to the distance to a static obstacle.
weight_velocity_obstacle_ratio: 0 # (default)
# Optimization weight for minimzing the distance to via-points
weight_viapoint: 50.0 # 1.0 (default)
# Optimization weight for preferring a specific turning direction
# (-> currently only activated if an oscillation is detected, see 'oscillation_recovery'.
optim.weight_prefer_rotdir: 50 # (default)
# Some special weights (currently weight_obstacle) are repeatedly
# scaled by this factor in each outer TEB iteration (weight_new = weight_old*factor)
weight_adapt_factor: 2.0 # 1.0 # GPT #
# Exponent for nonlinear obstacle cost (cost = linear_cost * obstacle_cost_exponent).
# Set to 1 to disable nonlinear cost (default)
obstacle_cost_exponent: 1.0 # (default)
# *************************************
# Parallel Planning in Distinctive Topologies
# *************************************
# Activate parallel planning in distinctive topologies
enable_homotopy_class_planning: false
# Activate multiple threading in order to plan each trajectory in a different thread
enable_multithreading: true # (default)
# If true, distinctive trajectories are explored using a simple left-right approach
# (pass each obstacle on the left or right side) for path generation,
# otherwise sample possible roadmaps randomly in a specified region between start and goal.
simple_exploration: false # (default)
# Specify the maximum number of distinctive trajectories taken into account
max_number_classes: 2
# Specify how much trajectory cost must a new candidate have w.r.t. a previously
# selected trajectory in order to be selected (selection if new_cost < old_cost*factor).
selection_cost_hysteresis: 1.0 # (default)
# Specify a cost reduction in the interval (0,1)
# for the trajectory in the equivalence class of the initial plan.
selection_prefer_initial_plan: 0.95 # (default)
# Extra scaling of obstacle cost terms just for selecting the 'best' candidate.
selection_obst_cost_scale: 100.0 # (default)
# Extra scaling of via-point cost terms just for selecting the 'best' candidate.
selection_viapoint_cost_scale: 1.0 # (default)
# If true, time cost is replaced by the total transition time.
selection_alternative_time_cost: false # (default)
# At each planning cycle, TEBs other than the current 'best' one will be randomly
# dropped with this probability. Prevents becoming 'fixated' on sub-optimal
# alternative homotopies.
selection_dropping_probability: 0.0 # (default)
# If simple_exploration is turned on, this parameter determines the distance on the
# left and right side of the obstacle at which a new keypoint will be cretead
obstacle_keypoint_offset: 0.1 # (default)
# Specify the value of the normalized scalar product between obstacle heading and
# goal heading in order to take them (obstacles) into account for exploration [0,1].
obstacle_heading_threshold: 0.45 # (default)
# Specify the number of samples generated for creating the roadmap graph
roadmap_graph_no_samples: 15 # (default)
# Random keypoints/waypoints are sampled in a rectangular region
# between start and goal. Specify the width of that region in meters
roadmap_graph_area_width: 6 # (default)
# The rectangular region is determined by the distance between start and goal.
# This parameter further scales the distance such that the geometric center remains equal!
roadmap_graph_area_length_scale: 1.0 # (default)
# Scale internal parameter (H-signature) that is used to distinguish
# between homotopy classe
h_signature_prescaler: 1.0 # (default)
# wo H-signatures are assumed to be equal, if both the difference
# of real parts and complex parts are below the specified threshold
h_signature_threshold: 0.1 # (default)
# Specify a time duration in seconds that needs to be expired before a
# switch to new equivalence class is allowed.
switching_blocking_period: 0.0 # (default)
# If true, all trajectories of different topologies are attached to the current
# set of via-points, otherwise only the trajectory sharing the same one as the
# initial/global plan.
viapoints_all_candidates: true # (default)
# Visualize the graph that is created for
# exploring distinctive trajectories (check marker message in rviz)
visualize_hc_graph: true
# If this value is bigger than 0, the trajectory and obstacles are visualized in
# 3d using the time as the z-axis scaled by this value. Most useful for dynamic obstacles.
visualize_with_time_as_z_axis_scale: 0.0 # (default)
# If enabled, the planner will discard the plans detouring
# backwards with respect to the best plan.
delete_detours_backwards: true # (default)
# A plan is considered a detour if its start orientation
# differs more than this from the best plan.
detours_orientation_tolerance: M_PI / 2.0 # (default)
# Length of the vector used to compute the start orientation of a plan.
length_start_orientation_vector: 0.4 # (default)
# Specify the maximum number of trajectories to try that are in the same
# homotopy class as the current trajectory (helps avoid local minima)
max_ratio_detours_duration_best_duration: 3.0 # (default)
# *************************************
# Recovery Parameters
# *************************************
# Allows the planner to shrink the horizon temporary (50%)
# in case of automatically detected issues.
shrink_horizon_backup: true # (default)
# Specify minimum duration for the reduced horizon in
# case an infeasible trajectory is detected
shrink_horizon_min_duration: 10.0 # (default)
# Try to detect and resolve oscillations between multiple solutions in the
#same equivalence class (robot frequently switches between left/right/forward/backwards)
oscillation_recovery: true # (default)
# Threshold for the average normalized linear velocity: if oscillation_v_eps and
# oscillation_omega_eps are not exceeded both, a possible oscillation is detected.
oscillation_v_eps: 0.1 # (default)
# Threshold for the average normalized angular velocity: if oscillation_v_eps and
# oscillation_omega_eps are not exceeded both, a possible oscillation is detected.
oscillation_omega_eps: 0.1 # (default)
# Minumum duration [sec] for which the recovery mode is activated after
# an oscillation is detected.
oscillation_recovery_min_duration: 10 # (default)
# Filter length/duration [sec] for the detection of oscillations.
oscillation_filter_duration: 10 # (default)
######################
GoToPlanner:
odom_topic: /loader/ugv1/odom
map_frame: map
# Trajectory
teb_autosize: True
dt_ref: 0.2
dt_hysteresis: 0.1
global_plan_overwrite_orientation: True
max_global_plan_lookahead_dist: 5.0
feasibility_check_no_poses: 5
allow_init_with_backwards_motion: false
# Robot
max_vel_x: 0.1
max_vel_x_backwards: 0.1
max_vel_theta: 0.5
acc_lim_x: 0.1
acc_lim_theta: 0.5
min_turning_radius: 0.0
# footprint_model:
# type: "line"
# line_start: [-0.930, 0.0] # [negative_length_component + width/2 + 0.05, 0.0]
# line_end: [0.450, 0.0] # [positive_length_component - width/2 - 0.05, 0.0]
# footprint_model:
# type: "line"
# line_start: [-0.930, 0.0] # [negative_length_component + width/2 + 0.05, 0.0]
# line_end: [1.10, 0.0] # [positive_length_component - width/2 - 0.05, 0.0]
# GoalTolerance
xy_goal_tolerance: 0.6
yaw_goal_tolerance: 0.1571
free_goal_vel: False
# Obstacles
min_obstacle_dist: 0.90
inflation_dist: 1.0
dynamic_obstacle_inflation_dist: 0.05
include_costmap_obstacles: True
costmap_obstacles_behind_robot_dist: 1.5
obstacle_poses_affected: 10
costmap_converter_plugin: ""
costmap_converter_spin_thread: True
costmap_converter_rate: 5
# Optimization
no_inner_iterations: 5
no_outer_iterations: 4
optimization_activate: True
optimization_verbose: False
penalty_epsilon: 0.09
weight_max_vel_x: 1
weight_max_vel_theta: 1
weight_acc_lim_x: 1
weight_acc_lim_theta: 1
weight_kinematics_nh: 1000
weight_kinematics_forward_drive: 1000
weight_kinematics_turning_radius: 0
weight_optimaltime: 1.0
weight_obstacle: 50
weight_viapoint: 50.0
weight_inflation: 0.1
weight_dynamic_obstacle: 10 # not in use yet
weight_shortest_path: 100.0
selection_alternative_time_cost: False # not in use yet
# Homotopy Class Planner
enable_homotopy_class_planning: False #True
enable_multithreading: True
simple_exploration: False
max_number_classes: 2 #4
roadmap_graph_no_samples: 15
roadmap_graph_area_width: 5
h_signature_prescaler: 0.5
h_signature_threshold: 0.1
obstacle_keypoint_offset: 0.1
obstacle_heading_threshold: 0.45
visualize_hc_graph: False
#ViaPoints
global_plan_viapoint_sep: 0.5 #negative if none
via_points_ordered: False #adhere to order of via points
#Feedback
publish_feedback: true
######################
UnloadPallet:
odom_topic: /loader/ugv1/odom
map_frame: map
# Trajectory
teb_autosize: True
dt_ref: 0.20
dt_hysteresis: 0.020
global_plan_overwrite_orientation: True
max_global_plan_lookahead_dist: 8.0
feasibility_check_no_poses: 5
allow_init_with_backwards_motion: true
# Robot
max_vel_x: 0.1
max_vel_x_backwards: 0.1
max_vel_theta: 0.5
acc_lim_x: 0.1
acc_lim_theta: 0.5
min_turning_radius: 0.0
# footprint_model:
# type: "line"
# line_start: [-0.930, 0.0] # [negative_length_component + width/2 + 0.05, 0.0]
# line_end: [0.450, 0.0] # [positive_length_component - width/2 - 0.05, 0.0]
# footprint_model:
# type: "line"
# line_start: [-0.930, 0.0] # [negative_length_component + width/2 + 0.05, 0.0]
# line_end: [1.10, 0.0] # [positive_length_component - width/2 - 0.05, 0.0]
# GoalTolerance
xy_goal_tolerance: 0.6
yaw_goal_tolerance: 0.5
free_goal_vel: False
# Obstacles
min_obstacle_dist: 0.90
inflation_dist: 1.0
dynamic_obstacle_inflation_dist: 0.05
include_costmap_obstacles: True
costmap_obstacles_behind_robot_dist: 5.0
obstacle_poses_affected: 30
costmap_converter_plugin: ""
costmap_converter_spin_thread: True
costmap_converter_rate: 5
# Optimization
no_inner_iterations: 5
no_outer_iterations: 4
optimization_activate: True
optimization_verbose: False
penalty_epsilon: 0.001
weight_max_vel_x: 1.0
weight_max_vel_theta: 1.0
weight_acc_lim_x: 1.0
weight_acc_lim_theta: 1.0
weight_kinematics_nh: 100.0
weight_kinematics_forward_drive: 0.01
weight_kinematics_turning_radius: 0.0
weight_optimaltime: 1.0
weight_obstacle: 50
weight_viapoint: 50.0
weight_inflation: 0.1
weight_dynamic_obstacle: 10 # not in use yet
weight_shortest_path: 50.0
selection_alternative_time_cost: False # not in use yet
# Homotopy Class Planner
enable_homotopy_class_planning: False #True
enable_multithreading: True
simple_exploration: False
max_number_classes: 2 #4
roadmap_graph_no_samples: 15
roadmap_graph_area_width: 5
h_signature_prescaler: 0.5
h_signature_threshold: 0.1
obstacle_keypoint_offset: 0.1
obstacle_heading_threshold: 0.45
visualize_hc_graph: False
#ViaPoints
global_plan_viapoint_sep: 0.5 #negative if none
via_points_ordered: False #adhere to order of via points
#Feedback
publish_feedback: true
ros_config/nav_move_base_flex/global_costmap_params.yaml 0 → 100644
View file @ 3fc4e072
global_costmap:
global_frame: map
robot_frame: base_link
robot_base_frame: base_link
transform_tolerance: 1.0
update_frequency: 2.0
publish_frequency: 2.0
rolling_window: false
width: 30.0
height: 30.0
resolution: 0.2
origin_x: 0.0
origin_y: 0.0
always_send_full_costmap: true
plugins:
- { name: global_static_layer, type: "costmap_2d::StaticLayer" }
- { name: global_inflation_layer, type: "costmap_2d::InflationLayer" }
# - { name: global_obstacle_layer_0, type: "spatio_temporal_voxel_layer/SpatioTemporalVoxelLayer" }
ros_config/nav_move_base_flex/local_costmap_params.yaml 0 → 100644
View file @ 3fc4e072
1local_costmap:
global_frame: map
robot_frame: base_link
robot_base_frame: base_link
transform_tolerance: 1.0
update_frequency: 2.0
publish_frequency: 2.0
rolling_window: true
width: 30.0
height: 30.0
resolution: 0.2
origin_x: 0.0
origin_y: 0.0
always_send_full_costmap: true
plugins:
- { name: local_static_layer_0, type: "costmap_2d::StaticLayer" }
- { name: local_inflation_layer, type: "costmap_2d::InflationLayer" }
# - { name: local_obstacle_layer_0, type: "spatio_temporal_voxel_layer/SpatioTemporalVoxelLayer" }
ros_config/nav_move_base_flex/mbf_costmap_nav.yaml 0 → 100644
View file @ 3fc4e072
#########################
### Static Parameters ###
#########################
robot_frame: base_link # the frame of the robot, which will be used to determine its position
map_frame: map # the global frame the robot is controlling in
global_frame: map
# force_stop_at_goal: false # force move base flex to stop the robot once the goal is reached
# force_stop_on_cancel: false # force move base flex to stop the robot on navigation cancellation
# mbf_tolerance_check: false # force move base flex to check for the goal tolerance
# dist_tolerance: 0.1 # distance tolerance to the given goal pose
# angle_tolerance: 0.175 # angle tolerance to the given goal pose
# tf_timeout: 1.0 # time before a timeout used for tf requests
#################################################
### Dynamically Reconfigurable MBF Parameters ###
#################################################
# Planners
planners: # global planner, e.g. navfn/NavfnROS
- name: GlobalPlanner
type: global_planner/GlobalPlanner
planner_frequency: $(arg planner_frequency) # the rate in Hz at which to run the planning loop
planner_max_retries: $(arg planner_max_retries) # how many times we will recall the planner in an attempt to find a valid plan before giving up
planner_patience: $(arg planner_patience) # how long the planner will wait in seconds in an attempt to find a valid plan before giving up
# Controllers
controllers: # list of controller, e.g. eband_local_planner/EBandPlannerROS
- name: TebLocalPlannerROS
type: teb_local_planner/TebLocalPlannerROS
# - name: GoToPlanner
# type: teb_local_planner/TebLocalPlannerROS
# - name: UnloadPallet
# type: teb_local_planner/TebLocalPlannerROS
controller_frequency: $(arg controller_frequency) # the rate in Hz at which to run the control loop and send velocity commands to the base
controller_max_retries: $(arg controller_max_retries) # how many times we will recall the controller in an attempt to find a valid command before giving up
controller_patience: $(arg controller_patience) # how long the controller will wait in seconds without receiving a valid control before giving up
# Oscillation
oscillation_timeout: 0.0 # how far in meters the robot must move to be considered not to be oscillating
oscillation_distance: 0.5 # how long in seconds to allow for oscillation before executing recovery behaviors
# Other
recovery_enabled: $(arg recovery_enabled) # enable the move_base_flex recovery behaviors to attempt to clear out space (for some reason the modern name does not work)
recovery_patience: 15.0 # how much time we allow recovery behaviors to complete before canceling (or stopping if cancel fails).
restore_defaults: false # restore to the original configuration
shutdown_costmaps: false # shutdown the costmaps of the node when move_base_flex is in an inactive state.
shutdown_costmaps_delay: 1.0 # how long in seconds to wait after last action before shutting down the costmaps.
# the remaining parameters do not seem to exist here: http://wiki.ros.org/move_base_flex#Navigation_Servers
# <param name="switch_controller_keyword_name" value="Docking"/> <!--the recovery will search for controllers with this name inside-->
# NOTES:
# - parameter sources: https://uos.github.io/mbf_docs/tutorials/beginner/parameters/mbf_parameters/ and
# https://wiki.ros.org/move_base_flex#Navigation_Servers.
#
# - controller_frequency can be set to a lower value (than the 10.0 as default) to prevent
# an high cmd_vel rate, which caused the robot to some discontinuous movements.
#
# - planner_frequency can be set to 0.0 in order to make the global_planner to run only when
# a new goal is received(it helps the robot to ignore drift during execution).
#
# - legacy "max_planning_retries" = mbf "planner_max_retries".
#
# - oscillation_timeout set to 0.0 in order to avoid the robot to get stuck when some oscillations are present.
#
# - planner_max_retries, controller_max_retries and recovery_enabled, these parameters were never set explicitly by us
# (source: https://wiki.ros.org/move_base_flex#Navigation_Servers and https://uos.github.io/mbf_docs/tutorials/beginner/parameters/mbf_parameters/)
#
# - shutdown_costmaps and shutdown_costmaps_delay, never set explecitily set by us before. Allow to shutdown costmaps,
# when move base is in an inactive state (might be good for optimisation).
#
# - legacy "recovery_behavior_enabled" = mbf "recovery_enabled".
#
# - there were legacy move base parameters in previous configuration: "conservative_reset_dist" and "clearing_rotation_allowed".
# Check the relay for more information https://github.com/magazino/move_base_flex/blob/master/mbf_costmap_nav/scripts/move_base_legacy_relay.py.
#
ros_config/nav_move_base_flex/planners.yaml 0 → 100644
View file @ 3fc4e072
GlobalPlanner:
# Lethal Cost (dynamic reconfigure)
lethal_cost: 253 # --- default ---
# Neutral Cost (dynamic reconfigure)
neutral_cost: 50 # --- default ---
# Factor to multiply each cost from costmap by (dynamic reconfigure)
cost_factor: 3.0 # --- default ---
# Specifies whether or not to visualize the potential area computed via a PointCloud2.
visualize_potential: true # --- default ---
# Publish Potential Costmap (dynamic reconfigure)
publish_potential: true # --- default ---
# How to set the orientation of each point (dynamic reconfigure)
# None=0, Forward=1, Interpolate=2, ForwardThenInterpolate=3,
# Backward=4, Leftward=5, Rightward=6
orientation_mode: 0 # --- default ---
# What window to use to determine the orientation based on the
# position derivative specified by the orientation mode (dynamic reconfigure)
orientation_window_size: 1 # --- default ---
# If for some reason, you want global_planner to exactly
# mirror the behavior of navfn, set this to true
old_navfn_behavior: false # --- default ---
# If true, create a path that follows the grid boundaries.
# Otherwise, use a gradient descent method.
use_grid_path: false # --- default ---
# If true, use the quadratic approximation of the potential.
# Otherwise, use a simpler calculation.
use_quadratic: true
# If true, use dijkstra's algorithm.
# Otherwise, A*.
use_dijkstra: true # --- default ---
# A tolerance on the goal point for the planner.
# The planner will attempt to create a plan that is as close to
# the specified goal as possible but no further than default_tolerance away
default_tolerance: 0.0
# Specifies whether or not to allow the planner to create plans that traverse unknown space. NOTE: if you are using a layered costmap_2d costmap with a voxel or obstacle layer, you must also set the track_unknown_space param for that layer to be true, or it will convert all your unknown space to free space (which planner will then happily go right through).
allow_unknown: false # *** change ***
# Outlines the global costmap with lethal obstacles.
# For the usage of a non static (rolling window) global costmap
# this needs to be set to fals
outline_map: true # --- default ---
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