# Revision history [back]

A couple of things:

1) I tried to run in simulation with similar parameters, I'll post them at the bottom of this post, and things worked for me.

2) Its not clear from the video you posted whether or not the local costmap's obstacles are being updated properly. This tutorial shows how to display navigation-relevant information in rviz. Specifically, I'd really like to know whether move_base/local_costmap/obstacles shows anything.

Here are the params I used in stage:

### costmap_common_params.yaml

#Set the tolerance we're willing to have for tf transforms
transform_tolerance: 0.3

#Obstacle marking parameters
obstacle_range: 2.5
max_obstacle_height: 2.0
raytrace_range: 3.0

#The footprint of the robot and associated padding
footprint: [[-0.325, -0.325], [-0.325, 0.325], [0.325, 0.325], [0.46, 0.0], [0.325, -0.325]]

#Cost function parameters
cost_scaling_factor: 10.0

#The cost at which a cell is considered an obstacle when a map is read from the map_server
lethal_cost_threshold: 100


### local_costmap_params.yaml

local_costmap:
#Set the global and robot frames for the costmap
global_frame: map

#Set the update and publish frequency of the costmap
update_frequency: 5.0
publish_frequency: 2.0

static_map: true
rolling_window: false


### global_costmap_params.yaml

global_costmap:
#Set the global and robot frames for the costmap
global_frame: /map

#Set the update and publish frequency of the costmap
update_frequency: 5.0
publish_frequency: 0.0

#We'll use a map served by the map_server to initialize this costmap
static_map: true
rolling_window: false


### base_local_planner_params.yaml

TrajectoryPlannerROS:
#Set the acceleration limits of the robot
acc_lim_th: 4.0
acc_lim_x: 3.7
acc_lim_y: 3.5

#Set the velocity limits of the robot
max_vel_x: 0.2
min_vel_x: 0.05
max_rotational_vel: 0.3
min_in_place_rotational_vel: 0.05

#The velocity the robot will command when trying to escape from a stuck situation
escape_vel: -0.1

#For this example, we'll use a holonomic robot
holonomic_robot: true

#Since we're using a holonomic robot, we'll set the set of y velocities it will sample
y_vels: [-0.3, -0.1, 0.1, 0.3]

#Set the tolerance on achieving a goal
xy_goal_tolerance: 0.1
yaw_goal_tolerance: 0.05

#We'll configure how long and with what granularity we'll forward simulate trajectories
sim_time: 1.7
sim_granularity: 0.025
vx_samples: 3
vtheta_samples: 20

#Parameters for scoring trajectories
goal_distance_bias: 0.8
path_distance_bias: 0.6
occdist_scale: 0.01