Perception-driven sparse graphs for optimal motion planning

TL;DR

This paper introduces a perception-driven sparse graph algorithm that integrates mapping and planning, enabling efficient, near-optimal motion planning in environments with limited sensing data and computational resources.

Contribution

It presents a novel iterative algorithm coupling perception and planning, achieving near-optimal trajectories with reduced sensing data and computational load.

Findings

Provides provably optimal trajectories in unmapped environments.

Reduces sensing data requirements significantly.

Demonstrates computational efficiency in experiments.

Abstract

Most existing motion planning algorithms assume that a map (of some quality) is fully determined prior to generating a motion plan. In many emerging applications of robotics, e.g., fast-moving agile aerial robots with constrained embedded computational platforms and visual sensors, dense maps of the world are not immediately available, and they are computationally expensive to construct. We propose a new algorithm for generating plan graphs which couples the perception and motion planning processes for computational efficiency. In a nutshell, the proposed algorithm iteratively switches between the planning sub-problem and the mapping sub-problem, each updating based on the other until a valid trajectory is found. The resulting trajectory retains a provable property of providing an optimal trajectory with respect to the full (unmapped) environment, while utilizing only a fraction of the sensing data in computational experiments.