A Globally Guided Dual-Arm Reactive Motion Controller for Coordinated Self-Handover in a Confined Domestic Environment.

Zihang Geng, Zhiyuan Yang, Wei Xu, Weichao Guo, Xinjun Sheng

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In one paragraph

Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Brain-Inspired Multi-Pathway Motion Decision-Making for Obstacle Avoidance of Humanoid Arms.Biomimetics (Basel, Switzerland) · 2026
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5 · Who and what money

Authors and funding

5 authors.

Zihang GengState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0009-0004-6971-9424

Zhiyuan YangState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Wei XuState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0002-3491-8923

Weichao GuoMeta Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China.

Xinjun ShengState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Funding

National Natural Science Foundation of China 52175021, 52375021Natural Science Foundation of Shanghai 23ZR1429700

6 · The paper itself

Abstract

Future humanoid robots will be widely deployed in our daily lives. Motion planning and control in an unstructured, confined, and human-centered environment utilizing dexterity and a cooperative ability of dual-arm robots is still an open issue. We propose a globally guided dual-arm reactive motion controller (GGDRC) that combines the strengths of global planning and reactive methods. In this framework, a global planner module with a prospective task horizon provides feasible guidance in a Cartesian space, and a local reactive controller module addresses real-time collision avoidance and coordinated task constraints through the exploitation of dual-arm redundancy. GGDRC extends the start-of-the-art optimization-based reactive method for motion-restricted dynamic scenarios requiring dual-arm cooperation. We design a pick-handover-place task to compare the performances of these two methods. Results demonstrate that GGDRC exhibits accurate collision avoidance precision (5 mm) and a high success rate (84.5%).

Indexed as

collision avoidancedual-arm coordinationglobal planninghumanoid robotmotion planningreactive control

Identifiers

PMID39451835

DOI10.3390/biomimetics9100629

PMCPMC11506656

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.