Python implementation of MPPI (Model Predictive Path-Integral) controller to understand the basic idea. Mandatory dependencies are numpy and matplotlib only.

A GPU implementation of Model Predictive Path Integral (MPPI) control that uses a probabilistic traversability model for planning risk-aware trajectories.

[ICRA2024] Stein Variational Guided Model Predictive Path Integral Control: Proposal and Experiments with Fast Maneuvering Vehicles

Adaptive importance sampling modification to MPPI

cuTAMP: Differentiable GPU-Parallelized Task and Motion Planning

A topology aware sampling-based global planner for dynamic 2D environments

A ROS package of a autonomous navigation method based on SAC and Bidirectional RRT* (Repository RL-RRT-Global-Planner).

Sampling based Model Predictive Control package for Model-Based RL research

off-road navigation simulator for benchmarking planning algorithms

Adaptive control for skid-steer robots using GP-enhanced MPPI for robust navigation and obstacle avoidance on diverse terrains.

CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces (AAMAS-22)

A ROS package of a path-planning method based on Bidirectional RRT*, which use the intermidiate points as the global information instead of the full path.

The OpenMORE project offers the essential tools for rapid robot path replanning during trajectory execution. It provides a robust architecture that manages both the replanning process and the execution of the trajectory. It also includes a library of sampling-based path replanning algorithms to efficiently handle dynamic changes in the environment.

A 2D simulation in Pygame of the paper "Randomized Kinodynamic Planning" by Steven M. LaValle, and James J. Kuffner, Jr.

This repository implements various Search Based (Heuristic and Incremental) and Sampling Based (Multi Query and Single Query) motion planning algorithms using ROS and turtlebot

Constrained Motion Planning Method with Latent Jumps

Time-Aware Probabilistic Roadmaps (TA-PRM*)

Implementations with interactive visualizations of multiple motion planning algorithms.

A 2D simulation in Pygame of the paper "Probabilistic roadmaps for path planning in high-dimensional configuration spaces" by L.E. Kavraki, P. Svestka, J.-C. Latombe, and M.H. Overmars.

A 2D simulation in Pygame of the paper "Rapidly-exploring random trees: A new tool for path planning" by Steven M. LaValle.