Motion Primitives based Path Planning with Rapidly-exploring Random Tree

TL;DR

This paper introduces a kinodynamically feasible path planning method using RRT combined with motion primitives, ensuring obstacle avoidance and dynamic constraints are satisfied in robot navigation.

Contribution

It integrates motion primitives into RRT to generate feasible paths without post-processing, improving kinodynamic planning efficiency.

Findings

Successfully generates collision-free, kinodynamically feasible paths in 2D simulations.

Demonstrates effectiveness across various robot models and motion primitives.

Ensures paths inherently satisfy robot dynamics constraints.

Abstract

We present an approach that generates kinodynamically feasible paths for robots using Rapidly-exploring Random Tree (RRT). We leverage motion primitives as a way to capture the dynamics of the robot and use these motion primitives to build branches of the tree with RRT. Since every branch is built using the robot's motion primitives that doesn't lead to collision with obstacles, the resulting path is guaranteed to satisfy the robot's kinodynamic constraints and thus be feasible for navigation without any post-processing on the generated trajectory. We demonstrate the effectiveness of our approach in simulated 2D environments using simple robot models with a variety of motion primitives.