Improving Human Situational Awareness and Planning Using a Human-Centric Velocity-Obstacle Algorithm
Aakash Bajpai, Alexander Lu, Kevin Choi, Rajan Tayal, Aaron J. Young, Anirban Mazumdar · ACM Transactions on Human-Robot Interaction · 2025
Human-robot teams in dynamic environments have the potential to leverage robot sensing and intelligence to augment human performance through motion suggestions. More specifically, we examine how humans can use external sensors (fixed or robotic) and a motion planning algorithm to help them navigate environments with dynamic obstacles. The novel human-centric velocity-obstacle (HCVO) algorithm suggests a feasible goal-oriented action while avoiding obstacles. Participants were placed in a custom virtual reality (VR) environment and tasked to follow a dynamic goal while avoiding collisions. We demonstrate, over N = 10 participants, that the HCVO algorithm’s guidance significantly improves safety compared to a base VO algorithm. We then examine the performance of N = 15 participants in three conditions: (1) no assistance/control, (2) a top-down drone-view of the entire environment, and 3) motion planner-informed suggestions. The core contributions of this research include (1) introducing and tuning of a human-centric velocity-obstacle (HCVO) algorithm, (2) demonstrating the benefits of the HCVO algorithm compared to a base VO algorithm, (3) demonstrating the benefits of the HCVO algorithm compared with a standard overhead drone view. Long term, the deployment of effective human-centric motion planners can make people safer from workplace to warzone. Code: https://github.com/ROAMR-GT/HCVO-Game . Video: https://www.youtube.com/watch?v=9ITD1GBBz24 .