Single-Agent On-line Path Planning in Continuous, Unpredictable and Highly Dynamic Environments

TL;DR

This thesis introduces a hybrid approach combining RRT variants and local search techniques for single-agent online path planning in unpredictable, dynamic environments, outperforming existing RRT extensions.

Contribution

It proposes a novel combination of RRT variants with local search and heuristics to improve path planning in highly dynamic, unpredictable environments.

Findings

Hybrid approach outperforms RRT extensions in dynamic scenarios

Combining initial RRT planning with local search enhances responsiveness

Simple heuristics improve overall path optimization

Abstract

This document is a thesis on the subject of single-agent on-line path planning in continuous,unpredictable and highly dynamic environments. The problem is finding and traversing a collision-free path for a holonomic robot, without kinodynamic restrictions, moving in an environment with several unpredictably moving obstacles or adversaries. The availability of perfect information of the environment at all times is assumed. Several static and dynamic variants of the Rapidly Exploring Random Trees (RRT) algorithm are explored, as well as an evolutionary algorithm for planning in dynamic environments called the Evolutionary Planner/Navigator. A combination of both kinds of algorithms is proposed to overcome shortcomings in both, and then a combination of a RRT variant for initial planning and informed local search for navigation, plus a simple greedy heuristic for optimization. We show that this combination of simple techniques provides better responses to highly dynamic environments than the RRT extensions.