Safe Motion Planning for a Mobile Robot Navigating in Environments Shared with Humans

TL;DR

This paper introduces a safe motion planning method for mobile robots in human-shared environments, utilizing a novel cost function integrated into a sampling-based algorithm to ensure safety and optimality.

Contribution

It proposes a new cost function based on human motion prediction models for safe navigation, integrated into the RRT*X algorithm, validated with real-world human trajectory data.

Findings

The approach guarantees asymptotic optimality in safe motion planning.

The cost function effectively incorporates human motion predictions.

Validation shows improved safety and efficiency in real-world scenarios.

Abstract

In this paper, a robot navigating an environment shared with humans is considered, and a cost function that can be exploited in $\text{RRT}^\text{X}$, a randomized sampling-based replanning algorithm that guarantees asymptotic optimality, to allow for a safe motion is proposed. The cost function is a path length weighted by a danger index based on a prediction of human motion performed using either a linear stochastic model, assuming constant longitudinal velocity and varying lateral velocity, and a GMM/GMR-based model, computed on an experimental dataset of human trajectories. The proposed approach is validated using a dataset of human trajectories collected in a real world setting.