A Framework for the Systematic Evaluation of Obstacle Avoidance and Object-Aware Controllers

TL;DR

This paper introduces a systematic framework for evaluating obstacle avoidance controllers in robots, focusing on kinematics, motion profiles, and virtual constraints, validated through experimental scenarios and comparative analysis.

Contribution

The work provides a novel framework for analyzing and benchmarking object-aware controllers, emphasizing design considerations often overlooked in existing methods.

Findings

Many controllers lack proper kinematic considerations.

Control points often lack continuity.

Movement profiles frequently lack stability.

Abstract

Real-time control is an essential aspect of safe robot operation in the real world with dynamic objects. We present a framework for the analysis of object-aware controllers, methods for altering a robot's motion to anticipate and avoid possible collisions. This framework is focused on three design considerations: kinematics, motion profiles, and virtual constraints. Additionally, the analysis in this work relies on verification of robot behaviors using fundamental robot-obstacle experimental scenarios. To showcase the effectiveness of our method we compare three representative object-aware controllers. The comparison uses metrics originating from the design considerations. From the analysis, we find that the design of object-aware controllers often lacks kinematic considerations, continuity of control points, and stability in movement profiles. We conclude that this framework can be used in the future to design, compare, and benchmark obstacle avoidance methods.