B$^{*}$: Efficient and Optimal Base Placement for Fixed-Base Manipulators

IF 5.3 2区 计算机科学 Q2 ROBOTICS
Zihang Zhao;Leiyao Cui;Sirui Xie;Saiyao Zhang;Zhi Han;Lecheng Ruan;Yixin Zhu
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引用次数: 0

Abstract

Proper base placement is crucial for task execution feasibility and performance of fixed-base manipulators, the dominant solution in robotic automation. Current methods rely on pre-computed kinematics databases generated through sampling to search for solutions. However, they face an inherent trade-off between solution optimality and computational efficiency when determining sampling resolution—a challenge that intensifies when considering long-horizon trajectories, self-collision avoidance, and task-specific requirements. To address these limitations, we present B$^{*}$, a novel optimization framework for determining the optimal base placement that unifies these multiple objectives without relying on pre-computed databases. B$^{*}$ addresses this inherently non-convex problem via a two-layer hierarchical approach: The outer layer systematically manages terminal constraints through progressively tightening them, particularly the base mobility constraint, enabling feasible initialization and broad solution space exploration. Concurrently, the inner layer addresses the non-convexities of each outer-layer subproblem by sequential local linearization, effectively transforming the original problem into a tractable sequential linear program (SLP). Comprehensive evaluations across multiple robot platforms and task complexities demonstrate the effectiveness of B$^{*}$: it achieves solution optimality five orders of magnitude better than sampling-based approaches while maintaining perfect success rates, all with reduced computational overhead. Operating directly in configuration space, B$^{*}$ not only solves the base placement problem but also enables simultaneous path planning with customizable optimization criteria, making it a versatile framework for various robotic motion planning challenges. B$^{*}$ serves as a crucial initialization tool for robotic applications, bridging the gap between theoretical motion planning and practical deployment where feasible trajectory existence is fundamental.
B$^{*}$:固定式机械臂的最优定位方法
固定基机械手是机器人自动化的主流解决方案,其正确的基座位置对任务执行的可行性和性能至关重要。目前的方法依赖于通过抽样生成的预先计算的运动学数据库来搜索解。然而,在确定采样分辨率时,它们面临着解决方案最优性和计算效率之间的内在权衡——当考虑到长视界轨迹、自我避免碰撞和特定任务要求时,这一挑战就会加剧。为了解决这些限制,我们提出了B$^{*}$,这是一个新的优化框架,用于确定统一这些多个目标的最佳基础位置,而不依赖于预先计算的数据库。B$^{*}$通过两层分层方法解决了这个固有的非凸问题:外层系统地管理终端约束,通过逐步收紧它们,特别是基本迁移约束,实现可行的初始化和广泛的解决方案空间探索。同时,内层通过序列局部线性化处理各外层子问题的非凸性,有效地将原问题转化为可处理的序列线性规划(SLP)。跨多个机器人平台和任务复杂性的综合评估证明了B$^{*}$的有效性:在保持完美成功率的同时,它比基于抽样的方法实现了五个数量级的最优解,所有这些都减少了计算开销。直接在构型空间中操作,B$^{*}$不仅解决了基座放置问题,而且还实现了具有可定制优化标准的同步路径规划,使其成为各种机器人运动规划挑战的通用框架。B$^{*}$作为机器人应用的关键初始化工具,弥合了理论运动规划和实际部署之间的差距,其中可行的轨迹存在是基本的。
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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