Sampling-based optimal kinodynamic planning with motion primitives

TL;DR

This paper introduces a sampling-based kinodynamic motion planner that uses a precomputed database of motion primitives within RRT* to reduce online computation and handle dynamic environments effectively.

Contribution

It presents a novel integration of motion primitives into RRT* with an offline database, balancing optimality and computational efficiency.

Findings

The planner is asymptotically optimal as grid resolution decreases.

Precomputed motion primitives reduce online planning time.

The approach effectively handles dynamic or partially known environments.

Abstract

This paper proposes a novel sampling-based motion planner, which integrates in RRT* (Rapidly exploring Random Tree star) a database of pre-computed motion primitives to alleviate its computational load and allow for motion planning in a dynamic or partially known environment. The database is built by considering a set of initial and final state pairs in some grid space, and determining for each pair an optimal trajectory that is compatible with the system dynamics and constraints, while minimizing a cost. Nodes are progressively added to the tree of feasible trajectories in the RRT* algorithm by extracting at random a sample in the gridded state space and selecting the best obstacle-free motion primitive in the database that joins it to an existing node. The tree is rewired if some nodes can be reached from the new sampled state through an obstacle-free motion primitive with lower cost. The computationally more intensive part of motion planning is thus moved to the preliminary offline phase of the database construction {at the price of some performance degradation due to gridding. Grid resolution can be tuned so as to compromise between (sub)optimality and size of the database. The planner is shown to be }asymptotically optimal as the grid resolution goes to zero and the number of sampled states grows to infinity.