The result of TEB calculation is very abnormal?

[xianglunkai]

[xianglunkai]

some warning: [ WARN] [1675151857.924004555]: Control loop missed its desired rate of 10.0000Hz... the loop actually took 0.2111 seconds [ WARN] [1675151857.928814744]: Map update loop missed its desired rate of 10.0000Hz... the loop actually took 0.1791 seconds

[xianglunkai]

TebLocalPlannerROS:

odom_topic: /odom map_frame: odom_combined

Trajectory

teb_autosize: True #优化期间允许改变轨迹的时域长度 dt_ref: 0.3 #局部路径规划的解析度# minimum 0.01 dt_hysteresis: 0.1 #允许改变的时域解析度的浮动范围， 一般为 dt_ref 的 10% 左右 minimum0.002 global_plan_overwrite_orientation: False #覆盖全局路径中局部路径点的朝向 max_global_plan_lookahead_dist: 3.0 #考虑优化的全局计划子集的最大长度 feasibility_check_no_poses: 3 #检测位姿可到达的时间间隔 minimum 0

Robot

max_vel_x: 0.4 #最大x前向速度 max_vel_y: 0 #最大y前向速度，非全向移动小车需要设置为0 max_vel_x_backwards: 0.4 #最大后退速度 max_vel_theta: 0.3 #最大转向角速度 acc_lim_x: 0.2 #最大x向加速度 acc_lim_y: 0 #最大y向加速度，非全向移动小车需要设置为0 acc_lim_theta: 0.3 #最大角加速度

阿克曼小车参数，非阿克曼小车设置为0

min_turning_radius: 0.1 #for not_akm # Min turning radius of the carlike robot (compute value using a model or adjust with rqt_reconfigure manually) wheelbase: 0.5 #for not_akm

cmd_angle_instead_rotvel: False #无论是不是阿克曼小车都设置为false，因为阿克曼小车启用了阿克曼速度转换包

true则cmd_vel/angular/z内的数据是舵机角度

footprint_model: # types: "point", "circular", "two_circles", "line", "polygon" 默认“point”类型 type: "polygon" #多边形类型for akm/mec，默认”point”。

type: "circular" #多边形类型for omni

line_start: [0.00, 0.0] # for type "line" senior_akm

line_end: [0.7, 0.0] # for type "line" top_akm_bs

radius: 0.3 # for type "circular"

front_offset: 0.2 #for type "two_circles"

front_rasius: 0.2 #for type "two_circles"

rear_offset : 0.2 #for type "two_circles"

rear_rasius : 0.2 #for type "two_circles"

差速系列

vertices: [[-0.1350, -0.1110], [-0.1350, 0.1110], [0.1350, 0.1110], [0.1350, -0.1110]] #多边形端点坐标 for mini_4wd

vertices: [[-0.62, -0.38], [-0.62, 0.48], [0.40, 0.48], [0.40, -0.38]] #多边形端点坐标 for mini_4wd xy_goal_tolerance: 0.4 #目标 xy 偏移容忍度 minimum 0.001 maximum 0.2 yaw_goal_tolerance: 0.2 #目标 角度 偏移容忍度 minimum 0.001 maximum 0.1 free_goal_vel: False #允许机器人以最大速度驶向目的地 complete_global_plan: True

Obstacles

min_obstacle_dist: 0.1 #和障碍物最小距离

inflation_dist: 0.05

include_costmap_obstacles: True #是否将动态障碍物预测为速度模型， costmap_obstacles_behind_robot_dist: 1.5 #限制机器人后方规划时考虑的局部成本地图障碍物 obstacle_poses_affected: 15 #障碍物姿态受影响0~30 costmap_converter_plugin: "" costmap_converter_spin_thread: True costmap_converter_rate: 5 include_dynamic_obstacles: True dynamic_obstacle_inflation_dist: 0.6 # 0.6

Optimization

no_inner_iterations: 5 no_outer_iterations: 4 optimization_activate: True optimization_verbose: False penalty_epsilon: 0.1 obstacle_cost_exponent: 4 weight_max_vel_x: 2 weight_max_vel_theta: 1 weight_acc_lim_x: 1 weight_acc_lim_theta: 1 weight_kinematics_nh: 1000 weight_kinematics_forward_drive: 1 weight_kinematics_turning_radius: 1 weight_optimaltime: 1 # must be > 0 weight_shortest_path: 0 weight_obstacle: 100 weight_inflation: 0.2 weight_dynamic_obstacle: 10 # not in use yet weight_dynamic_obstacle_inflation: 0.2 weight_viapoint: 1 weight_adapt_factor: 2

Homotopy Class Planner

enable_homotopy_class_planning: False enable_multithreading: True max_number_classes: 4 selection_cost_hysteresis: 1.0 selection_prefer_initial_plan: 0.95 selection_obst_cost_scale: 1.0 selection_alternative_time_cost: False

roadmap_graph_no_samples: 15 roadmap_graph_area_width: 5 roadmap_graph_area_length_scale: 1.0 h_signature_prescaler: 0.5 h_signature_threshold: 0.1 obstacle_heading_threshold: 0.45 switching_blocking_period: 0.0 viapoints_all_candidates: True delete_detours_backwards: True max_ratio_detours_duration_best_duration: 3.0 visualize_hc_graph: False visualize_with_time_as_z_axis_scale: False

Recovery

shrink_horizon_backup: True shrink_horizon_min_duration: 10 oscillation_recovery: False oscillation_v_eps: 0.1 oscillation_omega_eps: 0.1 oscillation_recovery_min_duration: 10 oscillation_filter_duration: 10

[skstudying]

我遇到了一样的问题，请问你找到问题出现的原因了吗？

[seanxu112]

I think ten is failing to solve the optimization. The goal seems to touch the wall, and you set min_obstacle_dist to 0.1, so there might not be a solution to this goal.