提出评估避障控制器的系统框架，助力安全机器人实时决策。
A Framework for the Systematic Evaluation of Obstacle Avoidance and Object-Aware Controllers
- 从运动学、轨迹规划、虚拟约束三方面构建评估体系。
- 实验证明现有控制器普遍缺乏运动连续性与稳定性。
- 适合研究机器人控制、自动驾驶系统的开发者参考。
实时控制是机器人在动态环境安全运行的关键。本文提出一种用于分析对象感知控制器的系统框架，通过调整机器人运动以预判和规避潜在碰撞。框架聚焦三个设计维度：运动学、运动轨迹及虚拟约束。分析基于基础机器人-障碍物实验场景进行行为验证。为展示方法有效性，对比了三种典型对象感知控制器，采用源于设计考虑的指标。结果表明，现有控制器常忽略运动学因素、控制点连续性及轨迹稳定性。结论指出，该框架未来可用于设计、比较和基准测试避障方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。