Human-robot co-navigation using anticipatory indicators of human walking motion

TLDR

Robots operating in human‑centric environments must safely navigate around people, requiring the ability to sense and predict human motion trajectories. This study demonstrates that biomechanical turn indicators of human walking motions are statistically significant and can be used as features to predict motion trajectories. We collected motion‑capture data with predefined goals to train and test a prediction algorithm that incorporates these turn indicators, and we validated its closed‑loop performance using an existing motion‑planning algorithm in dynamic settings. Using anticipatory turn indicators improves prediction accuracy and enables effective human‑robot co‑navigation when paired with suitable planning algorithms.

Abstract

Mobile, interactive robots that operate in human-centric environments need the capability to safely and efficiently navigate around humans. This requires the ability to sense and predict human motion trajectories and to plan around them. In this paper, we present a study that supports the existence of statistically significant biomechanical turn indicators of human walking motions. Further, we demonstrate the effectiveness of these turn indicators as features in the prediction of human motion trajectories. Human motion capture data is collected with predefined goals to train and test a prediction algorithm. Use of anticipatory features results in improved performance of the prediction algorithm. Lastly, we demonstrate the closed-loop performance of the prediction algorithm using an existing algorithm for motion planning within dynamic environments. The anticipatory indicators of human walking motion can be used with different prediction and/or planning algorithms for robotics; the chosen planning and prediction algorithm demonstrates one such implementation for human-robot co-navigation.