Scholarlink

<p>To meet the growing demand for autonomous assembly in unstructured environments, such as field exploration, disaster recovery, and space assembly, this study proposes a fixture-free assembly system requiring a single robotic arm. We present a dual-layer framework comprising two components: An upper layer assembly sequence planner that generates feasible optimal assembly sequences through a branch-and-bound guided search algorithm, which incorporates geometric constraints, gravitational stability analysis, and load support capacity; a lower layer assembly manipulation planner that selects appropriate skills from predefined skill libraries while ensuring motion feasibility through real-time trajectory planning. In particular, we develop a novel skill organization mechanism that comprehensively considers motion constraints of multiple skill primitives during parameter configuration. Experimental validation in non-standardized environments demonstrates the robustness and effectiveness in managing complex, long-horizon assembly tasks, achieving notable improvements in efficiency and adaptability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Fixture-free assembly sequence and manipulation planning for single-arm robots

  • 期刊论文

  • 发表日期：

    2025年8月22日

查看全文
  • Tao Lin,
  • Chengfei Yue,
  • Peiji Wang,
  • Haoyong Yu,
  • Xibin Cao

摘要

To meet the growing demand for autonomous assembly in unstructured environments, such as field exploration, disaster recovery, and space assembly, this study proposes a fixture-free assembly system requiring a single robotic arm. We present a dual-layer framework comprising two components: An upper layer assembly sequence planner that generates feasible optimal assembly sequences through a branch-and-bound guided search algorithm, which incorporates geometric constraints, gravitational stability analysis, and load support capacity; a lower layer assembly manipulation planner that selects appropriate skills from predefined skill libraries while ensuring motion feasibility through real-time trajectory planning. In particular, we develop a novel skill organization mechanism that comprehensively considers motion constraints of multiple skill primitives during parameter configuration. Experimental validation in non-standardized environments demonstrates the robustness and effectiveness in managing complex, long-horizon assembly tasks, achieving notable improvements in efficiency and adaptability.