Model Predictive Path Integral Controller

Overview

Navigation2 Controller plugin. Currently testing on ros2 foxy.

This is a controller (local trajectory planner) that implements model predictive path integral control to track a path with collision avoidance.

The main idea of the algorithm is to sample batch of control sequences with specified time step for each control, Having inital state of robot (pose, velocity) and batch of controls use iteratively "model" to predict real velocities for each time step in batch.

this can be explained as follows V(t+1) = M(t), where

• V(t+1) - predicted velocities of batch at time step t + 1
• M(T) - Function that predicts real velocities at t + 1 step, by given velocities and control actions at t step.

Then velocities integrated to get trajectories. For each trajectory, the cost function is calculated. All control sequences are weighted by trajectories costs using softmax function to get final control sequence.

Dependencies

This uses the usual ROS tools for dependency management, so please use rosdep to install the dependencies.

Note: If running on Ubuntu 20.04 or other OS's that xtensor is not released in binary form, please manually install xtensor v 0.24.0 and xtl v 0.7.0. These are simply headers so the install process is trivially short, unfortunately the xtensor project isn't available in package managers in some common-place operating systems (albeit, all necessary ROS OS versions) so you may be required to do this yourself if building from source.

git clone [email protected]:xtensor-stack/xtensor.git -b 0.24.0
cd xtensor
mkdir build
cd build
cmake ..
sudo make install

git clone [email protected]:xtensor-stack/xtl.git -b 0.7.0
cd xtl
mkdir build
cd build
cmake ..
sudo make install

# Optional
git clone [email protected]:xtensor-stack/xsimd.git -b 8.0.5
cd xsimd
mkdir build
cmake ..
sudo make install

Configuration

Controller params

Parameter	Type	Definition
iteration_count	int	Iteration count in MPPI algorithm
max_robot_pose_search_dist	double	Upper bound on integrated distance along the global plan to search for the closest pose to the robot pose. This should be left as the default unless there are paths with loops and intersections that do not leave the local costmap, in which case making this value smaller is necessary to prevent shortcutting.
transform_tolerance	double	TF tolerance to transform poses
batch_size	int	Count of randomly sampled trajectories
time_steps	int	Number of time steps (points) in each sampled trajectory
model_dt	double	Time interval between two sampled points in trajectories
vx_std	double	Sampling standart deviation for VX
vy_std	double	Sampling standart deviation for VY
wx_std	double	Sampling standart deviation for WX
vx_max	double	Max VX
vy_max	double	Max VY
wz_max	double	Max WZ
temperature	double	Selectiveness of trajectories by their costs (The closer this value to 0, the "more" we take in considiration controls with less cost), 0 mean use control with best cost, huge value will lead to just taking mean of all trajectories withou cost consideration
visualize	bool	Use visualization
motion_model	string	Type of model [DiffDrive, Omni, Ackermann]

CriticScorer params

Parameter	Type	Definition
critics	string list	Critics (plugins) names

GoalCritic params

Parameter	Type	Definition
goal_weight	double
goal_power	int

GoalAngleCritic params

Parameter	Type	Definition
goal_angle_cost_weight	double
goal_angle_cost_power	int
threshold_to_consider_goal_angle	double	Minimal distance between robot and goal above which angle goal cost considered

PathAngleCritic params

Parameter	Type	Definition
path_angle_cost_power	double
path_angle_cost_weight	int

[Approx]ReferenceTrajectoryCritic params

Parameter	Type	Definition
reference_cost_weight	double
reference_cost_power	int

ObstaclesCritic params

Parameter	Type	Definition
consider_footprint	bool
obstacle_cost_weight	double
obstacle_cost_power	int
inflation_cost_scaling_factor	int	Must be set accurately according to inflation layer params
inflation_radius	double	Must be set accurately according to inflation layer params

PreferForwardCritic params

Parameter	Type	Definition
prefer_forward_cost_weight	double
prefer_forward_cost_power	int

XML configuration example

controller_server:
  ros__parameters:
    FollowPath:
      plugin: "mppi::Controller"
      time_steps: 15
      model_dt: 0.1
      batch_size: 400
      vx_std: 0.1
      vy_std: 0.1
      wz_std: 0.6
      vx_max: 0.5
      vy_max: 0.5
      wz_max: 1.3
      iteration_count: 2
      temperature: 0.25
      motion_model: "DiffDrive"
      visualize: false
      critics: [ "GoalCritic", "PreferForwardCritic", GoalAngleCritic", "PathAngleCritic", "ReferenceTrajectoryCritic", "ObstaclesCritic" ]
      GoalCritic:
        goal_cost_power: 1
        goal_cost_weight: 8.0
      GoalAngleCritic:
        goal_angle_cost_power: 1
        goal_angle_cost_weight: 15.0
        threshold_to_consider_goal_angle: 0.20
      ReferenceTrajectoryCritic:
        reference_cost_power: 1
        reference_cost_weight: 5.0
      ObstaclesCritic:
        consider_footprint: true
        obstacle_cost_power: 1
        obstacle_cost_weight: 0.5
      PathAngleCritic:
        path_angle_cost_power: 1
        path_angle_cost_weight: 0.5
      PreferForwardCritic:
        prefer_forward_cost_power: 1
        prefer_forward_cost_weight: 50.0

Topics

Topic	Type	Description
trajectories	visualization_msgs/MarkerArray	Randomly generated trajectories, including resulting control sequence
transformed_global_plan	nav_msgs/Path	Part of global plan considered by local planner

References

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