Considering Human Behavior in Motion Planning for Smooth Human-Robot Collaboration in Close Proximity

TL;DR

This paper introduces a human-aware motion planning method for close-proximity human-robot collaboration, utilizing occupancy maps and cost functions to enhance safety, efficiency, and human-friendliness in both simulated and real environments.

Contribution

It presents a novel approach that integrates human behavior preferences into robot trajectory planning using occupancy maps and optimization-based cost functions.

Findings

Significantly increased task success rates in collaboration scenarios

Improved safety and human-friendliness of robot trajectories

Task-specific adjustment of cost functions enhances performance

Abstract

It is well-known that a deep understanding of co-workers' behavior and preference is important for collaboration effectiveness. In this work, we present a method to accomplish smooth human-robot collaboration in close proximity by taking into account the human's behavior while planning the robot's trajectory. In particular, we first use an occupancy map to summarize human's movement preference over time, and such prior information is then considered in an optimization-based motion planner via two cost items as introduced in [1]: 1) avoidance of the workspace previously occupied by human, to eliminate the interruption and to increase the task success rate; 2) tendency to keep a safe distance between the human and the robot to improve the safety. In the experiments, we compare the collaboration performance among planners using different combinations of human-aware cost items, including the avoidance factor, both the avoidance and safe distance factor, and a baseline where no human-related factors are considered. The trajectories generated are tested in both simulated and real-world environments, and the results show that our method can significantly increase the collaborative task success rates and is also human-friendly. Our experimental results also show that the cost functions need to be adjusted in a task specific manner to better reflect human's preference.