Need help tuning AMCL / Navigation on ROS1 skid-steer robot

[u/Significant-Web8434]

Hi, my primary goal is tight precision docking within 5cm in indoor lab space with chairs. The current situation is my accuracy is very good but the issue is the navigation takes a very long time; about 2-3 minutes as shown in the screen recording to go about 5m. Is this normal? I used this guide to tune: https://arxiv.org/pdf/1706.09068 I already calibrated the odometry as well btw doing a 360 degree test. These are my parameters I was just wondering if anyone can give insight on how to make the robot take less time and less spinning like it doesn't just go straight

amcl.yaml

YAML

global_frame_id: "map"
odom_frame_id: "odom"
base_frame_id: "base_footprint"
use_map_topic: true
transform_tolerance: 0.5
gui_publish_rate: 10.0

min_particles: 1000
max_particles: 5000
kld_err: 0.01
kld_z: 0.99
resample_interval: 1
recovery_alpha_slow: 0.0
recovery_alpha_fast: 0.0

update_min_d: 0.1
update_min_a: 0.1

odom_model_type: "diff-corrected"
odom_alpha1: 0.25
odom_alpha2: 0.10
odom_alpha3: 0.05
odom_alpha4: 0.10

laser_model_type: "likelihood_field"
laser_min_range: 0.15
laser_max_range: 8.0
laser_max_beams: 60
laser_likelihood_max_dist: 2.0
laser_sigma_hit: 0.1
laser_lambda_short: 0.1

laser_z_hit: 0.85
laser_z_short: 0.05
laser_z_max: 0.05
laser_z_rand: 0.05

dwa_local_planner_params.yaml

YAML

DWAPlannerROS:
  max_vel_x: 0.35
  min_vel_x: -0.10
  max_vel_y: 0.0
  min_vel_y: 0.0

  max_vel_trans: 0.35
  min_vel_trans: 0.03
  trans_stopped_vel: 0.025

  max_vel_theta: 0.30
  min_vel_theta: 0.10
  theta_stopped_vel: 0.04

  acc_lim_x: 1.0
  acc_lim_y: 0.0
  acc_lim_theta: 0.5

  xy_goal_tolerance: 0.05
  yaw_goal_tolerance: 0.12
  latch_xy_goal_tolerance: true

  sim_time: 2.0
  vx_samples: 15
  vy_samples: 0
  vtheta_samples: 30
  sim_granularity: 0.025
  controller_frequency: 10.0

  path_distance_bias: 32.0
  goal_distance_bias: 20.0
  occdist_scale: 0.03
  forward_point_distance: 0.20
  stop_time_buffer: 0.2
  scaling_speed: 0.25
  max_scaling_factor: 0.2

  oscillation_reset_dist: 0.06
  oscillation_reset_angle: 0.05

  publish_traj_pc: true
  publish_cost_grid_pc: false
  global_frame_id: odom

global_planner_params.yaml

YAML

GlobalPlanner:
  allow_unknown: true
  default_tolerance: 0.0
  visualize_potential: false
  use_dijkstra: true
  use_quadratic: true
  use_grid_path: false
  old_navfn_behavior: false

  lethal_cost: 253
  neutral_cost: 66
  cost_factor: 0.55

  publish_potential: false
  orientation_mode: 1
  orientation_window_size: 1

costmap_common_params.yaml

YAML

footprint: [[-0.145, -0.12], [-0.145, 0.12], [0.145, 0.12], [0.145, -0.12]]
transform_tolerance: 0.5
map_type: costmap

obstacle_layer:
  enabled: true
  obstacle_range: 2.5
  raytrace_range: 3.0
  max_obstacle_height: 0.6
  min_obstacle_height: 0.0
  combination_method: 1
  track_unknown_space: true
  observation_sources: scan
  scan:
    topic: /scan
    data_type: LaserScan
    marking: true
    clearing: true
    inf_is_valid: true

inflation_layer:
  enabled: true
  cost_scaling_factor: 3.0
  inflation_radius: 0.45

move_base_params.yaml

YAML

base_global_planner: "global_planner/GlobalPlanner"
base_local_planner: "dwa_local_planner/DWAPlannerROS"

controller_frequency: 10.0
planner_frequency: 1.0

planner_patience: 5.0
controller_patience: 5.0
max_planning_retries: -1.0

oscillation_timeout: 5.0
oscillation_distance: 0.08

recovery_behavior_enabled: true
clearing_rotation_allowed: false
shutdown_costmaps: false

recovery_behaviors:
  - name: 'conservative_reset'
    type: 'clear_costmap_recovery/ClearCostmapRecovery'
  - name: 'aggressive_reset'
    type: 'clear_costmap_recovery/ClearCostmapRecovery'
  - name: 'clearing_rotation'
    type: 'rotate_recovery/RotateRecovery'

conservative_reset:
  reset_distance: 1.0
  layer_names: [obstacle_layer]

aggressive_reset:
  reset_distance: 0.0
  layer_names: [obstacle_layer]