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

2025-10-28

AI Summary

To address the lack of systematic benchmarks for evaluating the safety of obstacle-avoidance controllers in dynamic environments, this paper proposes the first three-dimensional evaluation framework tailored to object-perception-based controllers. The framework systematically assesses three core dimensions: kinematic completeness, control-point continuity, and trajectory stability. Grounded in representative robot–obstacle interaction scenarios, it introduces an experiment-driven, quantitative evaluation methodology to comparatively analyze three mainstream controller classes. Results expose common deficiencies across controllers—particularly in motion smoothness and trajectory stability. Crucially, this work establishes the first reproducible, extensible, and standardized assessment infrastructure for obstacle avoidance. By unifying evaluation criteria and metrics, it provides both theoretical foundations and practical tools for performance benchmarking, defect diagnosis, and safety-oriented optimization of navigation algorithms.

Technology Category

• Natural Language Processing: Safety and Robustness
• Intelligent Robots: Motion and Path Planning
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• Evaluating obstacle avoidance and object-aware robot controllers systematically
• Analyzing kinematic, motion profile, and virtual constraint design considerations
• Comparing controller performance using fundamental robot-obstacle scenarios

Innovation

• Framework evaluates object-aware controllers systematically
• Analyzes kinematics, motion profiles, and virtual constraints
• Uses robot-obstacle scenarios for behavior verification