An Integrated Localisation, Motion Planning and Obstacle Avoidance Algorithm in Belief Space

TL;DR

This paper introduces a novel probabilistic approach for robot localization, motion planning, and obstacle avoidance in belief space, accounting for sensor and actuation uncertainties to enhance safety and efficiency.

Contribution

It presents a new method to incorporate uncertainties into collision probability estimation and trajectory synthesis, with proven convergence and error bounds.

Findings

Collision probability expressed as an infinite series with proven convergence

Effective trajectory synthesis satisfying collision probability bounds

Demonstrated approach in simulation domains with roadmap-based strategies

Abstract

As robots are being increasingly used in close proximity to humans and objects, it is imperative that robots operate safely and efficiently under real-world conditions. Yet, the environment is seldom known perfectly. Noisy sensors and actuation errors compound to the errors introduced while estimating features of the environment. We present a novel approach (1) to incorporate these uncertainties for robot state estimation and (2) to compute the probability of collision pertaining to the estimated robot configurations. The expression for collision probability is obtained as an infinite series and we prove its convergence. An upper bound for the truncation error is also derived and the number of terms required is demonstrated by analyzing the convergence for different robot and obstacle configurations. We evaluate our approach using two simulation domains which use a roadmap-based strategy to synthesize trajectories that satisfy collision probability bounds.