ForwardDLO: Model-Based Bimanual Shape Matching of Unconstrained Deformable Linear Objects
研究提出ForwardDLO模型，通过预测绳索各段位移解决机器人双臂操控非固定柔性线性物体形状匹配问题。
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Sequential Object Placement Optimization with Convex Decomposition
本文通过引入SOPO-CD框架，将物体放置问题转化为可微非线性优化问题，解决了机器人在处理不规则形状物体时的高效打包难题。
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Coordinated Multi-Robot Disassembly for Makespan Optimization of Large-Scale Assemblies
This work addresses the challenge of prolonged makespan in multi-robot collaborative disassembly within confined spaces, where motion conflicts frequently occur. The authors propose CoMuDi, a novel approach that deeply integrates spatiotemporal RRT* (ST-RRT*) into multi-robot disassembly planning for the first time. CoMuDi models the assembly using a dependency graph to generate composite tasks, propagates temporal constraints to coordinate robot actions, and leverages ST-RRT* to optimize the execution time of individual tasks, thereby minimizing overall makespan. Evaluated across six benchmark scenarios involving up to 49 parts and nine robots, CoMuDi significantly improves planning success rates while effectively reducing both makespan and robot idle time.
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ForwardDLO: Model-Based Bimanual Shape Matching of Unconstrained Deformable Linear Objects
研究提出ForwardDLO模型，通过预测绳索各段位移解决机器人双臂操控非固定柔性线性物体形状匹配问题。
0 citationsRead paper
Sequential Object Placement Optimization with Convex Decomposition
本文通过引入SOPO-CD框架，将物体放置问题转化为可微非线性优化问题，解决了机器人在处理不规则形状物体时的高效打包难题。
0 citationsRead paper
Coordinated Multi-Robot Disassembly for Makespan Optimization of Large-Scale Assemblies
This work addresses the challenge of prolonged makespan in multi-robot collaborative disassembly within confined spaces, where motion conflicts frequently occur. The authors propose CoMuDi, a novel approach that deeply integrates spatiotemporal RRT* (ST-RRT*) into multi-robot disassembly planning for the first time. CoMuDi models the assembly using a dependency graph to generate composite tasks, propagates temporal constraints to coordinate robot actions, and leverages ST-RRT* to optimize the execution time of individual tasks, thereby minimizing overall makespan. Evaluated across six benchmark scenarios involving up to 49 parts and nine robots, CoMuDi significantly improves planning success rates while effectively reducing both makespan and robot idle time.
0 citationsRead paper