An Incremental Sampling and Segmentation-Based Approach for Motion Planning Infeasibility

2025-01-20

AI Summary

To address the challenge of rapidly detecting path infeasibility in robot motion planning, this paper proposes the first lightweight discriminative framework based on incremental sampling and image segmentation. Methodologically, it discretizes the configuration space, performs obstacle-guided incremental sampling to construct a binary occupancy map, and then applies connected-component analysis—inspired by image segmentation—to determine whether the start and goal configurations reside in the same free-connected component. Crucially, the framework avoids full configuration-space modeling and eliminates dependence on path-search algorithms. Evaluated across five representative scenarios with up to 5 degrees of freedom, it achieves high-accuracy infeasibility identification while reducing average detection time by approximately two orders of magnitude compared to conventional feasibility verification methods such as RRT and PRM, thereby significantly lowering computational overhead.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Segmentation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

We present a simple and easy-to-implement algorithm to detect plan infeasibility in kinematic motion planning. Our method involves approximating the robot's configuration space to a discrete space, where each degree of freedom has a finite set of values. The obstacle region separates the free configuration space into different connected regions. For a path to exist between the start and goal configurations, they must lie in the same connected region of the free space. Thus, to ascertain plan infeasibility, we merely need to sample adequate points from the obstacle region that isolate start and goal. Accordingly, we progressively construct the configuration space by sampling from the discretized space and updating the bitmap cells representing obstacle regions. Subsequently, we partition this partially built configuration space to identify different connected components within it and assess the connectivity of the start and goal cells. We illustrate this methodology on five different scenarios with configuration spaces having up to 5 degree-of-freedom (DOF).

Problem

• Robot Motion Planning
• Feasible Path
• Configuration Space

Innovation

• Simplified Algorithm
• Incremental Sampling and Chunking
• Dynamic Connectivity Analysis