HIRO: Heuristics Informed Robot Online Path Planning Using Pre-computed Deterministic Roadmaps

TL;DR

HIRO is a novel robot path planning method that uses static environment data for heuristics, guiding a search in dynamic environments to find collision-free paths faster than existing methods.

Contribution

The paper introduces a new heuristics-informed online path planning approach that combines deterministic roadmaps with fuzzy collision checking for improved efficiency in dynamic settings.

Findings

HIRO significantly accelerates path planning in dynamic environments.

It outperforms baseline methods in simulation and real-world tests.

The approach maintains high success rates in collision avoidance.

Abstract

With the goal of efficiently computing collision-free robot motion trajectories in dynamically changing environments, we present results of a novel method for Heuristics Informed Robot Online Path Planning (HIRO). Dividing robot environments into static and dynamic elements, we use the static part for initializing a deterministic roadmap, which provides a lower bound of the final path cost as informed heuristics for fast path-finding. These heuristics guide a search tree to explore the roadmap during runtime. The search tree examines the edges using a fuzzy collision checking concerning the dynamic environment. Finally, the heuristics tree exploits knowledge fed back from the fuzzy collision checking module and updates the lower bound for the path cost. As we demonstrate in real-world experiments, the closed-loop formed by these three components significantly accelerates the planning procedure. An additional backtracking step ensures the feasibility of the resulting paths. Experiments in simulation and the real world show that HIRO can find collision-free paths considerably faster than baseline methods with and without prior knowledge of the environment.