Intelligent Collision Management in Dynamic Environments for Human-Centered Robots
Kwan Suk Kim

TL;DR
This paper explores advanced collision management strategies for human-centered robots, focusing on real-time sensing, motion planning, and collision prediction to enhance safety and prevent accidents in dynamic human environments.
Contribution
It introduces novel techniques combining collision response, motion planning, and predictive reasoning to improve robot safety and interaction in human environments.
Findings
Developed real-time collision detection and response methods.
Proposed predictive models for future collision prevention.
Provided guidelines for safe robot operation in human spaces.
Abstract
In this context, a major focus of this thesis is on unintentional collisions, where a straight goal is to eliminate injury from users and passerby's via realtime sensing and control systems. A less obvious focus is to combine collision response with tools from motion planning in order to produce intelligent safety behaviors that ensure the safety of multiple people or objects. Yet, an even more challenging problem is to anticipate future collisions between objects external to the robot and have the robot intervene to prevent imminent accidents. In this dissertation, we study all of these sophisticated flavors of collision reaction and intervention. We investigate in-depth multiple key and interesting topics related to collisions and safety of mobile robots and robotic manipulators operating in human environments. Overall we deeply investigate collisions from many perspectives and…
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Taxonomy
TopicsRobotic Path Planning Algorithms
