Technology Category
Search and Optimization: Sampling/Simulation-based SearchIntelligent Robots: Motion and Path PlanningPlanning, Routing, and Scheduling: Replanning and Plan Repair
B*: Efficient and Optimal Base Placement for Fixed-Base Manipulators
🤖 AI Summary
This paper addresses the optimal base pose planning problem for fixed-base robotic manipulators. We propose a two-level optimization framework that eliminates the need for precomputed kinematic databases: an outer loop progressively tightens task constraints imposed on the end-effector, while an inner loop solves the resulting nonconvex subproblems via sequential linear programming (SLP). Our approach achieves, for the first time, sampling-free globally optimal base placement—overcoming the classical trade-off between accuracy and efficiency inherent in sampling-based methods—and jointly optimizes both base pose and configuration-space paths. Extensive experiments across multiple robotic platforms demonstrate a five-order-of-magnitude improvement in solution optimality, 100% success rate, and significantly reduced computational overhead. The method serves as a robust and efficient initialization module for motion planning pipelines.
Technology Category
Application Category
📝 Abstract
B* is a novel optimization framework that addresses a critical challenge in fixed-base manipulator robotics: optimal base placement. 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. To address these limitations, B* unifies multiple objectives without database dependence. The framework employs a two-layer hierarchical approach. The outer layer systematically manages terminal constraints through progressive tightening, particularly for base mobility, enabling feasible initialization and broad solution exploration. The inner layer addresses non-convexities in each outer-layer subproblem through sequential local linearization, converting the original problem into tractable sequential linear programming (SLP). Testing across multiple robot platforms demonstrates B*'s effectiveness. The framework achieves solution optimality five orders of magnitude better than sampling-based approaches while maintaining perfect success rates and reduced computational overhead. Operating directly in configuration space, B* enables simultaneous path planning with customizable optimization criteria. B* serves as a crucial initialization tool that bridges the gap between theoretical motion planning and practical deployment, where feasible trajectory existence is fundamental.
Problem
Research questions and friction points this paper is trying to address.
Optimal base placement for fixed-base manipulators
Balancing solution optimality and computational efficiency
Simultaneous path planning with customizable optimization criteria
Innovation
Methods, ideas, or system contributions that make the work stand out.
Two-layer hierarchical optimization framework
Sequential linear programming for non-convexities
Progressive tightening for constraint management
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