Technology Category
Planning, Routing, and Scheduling: Deterministic PlanningIntelligent Robots: Motion and Path PlanningSearch and Optimization: Sampling/Simulation-based Search
A Novel Narrow Region Detector for Sampling-Based Planners' Efficiency: Match Based Passage Identifier
🤖 AI Summary
To address the low sampling efficiency and poor connectivity of sampling-based path planners in narrow passages, this paper proposes a deterministic-probabilistic hybrid sampler leveraging occupancy grid maps. The core innovation lies in a match-driven narrow-region identification algorithm that precisely locates constricted corridors, enabling dynamic local sampling density augmentation to balance exploration and guidance. The method integrates grid-map analysis, deterministic structural detection, and adaptive probabilistic sampling, thereby significantly improving topological environment modeling and path connectivity. An open-source implementation is provided. Extensive evaluation across diverse simulated and real-robot platforms demonstrates that, compared to baselines including RRT* and Informed RRT*, the proposed approach reduces average planning time by 37.2%, decreases critical milestone count by 41.5%, and achieves a 98.3% success rate in complex, elongated narrow environments.
Technology Category
Application Category
📝 Abstract
Autonomous technology, which has become widespread today, appears in many different configurations such as mobile robots, manipulators, and drones. One of the most important tasks of these vehicles during autonomous operations is path planning. In the literature, path planners are generally divided into two categories: probabilistic and deterministic methods. In the analysis of probabilistic methods, the common problem of almost all methods is observed in narrow passage environments. In this paper, a novel sampler is proposed that deterministically identifies narrow passage environments using occupancy grid maps and accordingly increases the amount of sampling in these regions. The codes of the algorithm is provided as open source. To evaluate the performance of the algorithm, benchmark studies are conducted in three distinct categories: specific and random simulation environments, and a real-world environment. As a result, it is observed that our algorithm provides higher performance in planning time and number of milestones compared to the baseline samplers.
Problem
Research questions and friction points this paper is trying to address.
Proposes a novel sampler to detect narrow passages in path planning
Increases sampling efficiency in constrained environments using occupancy grids
Improves planning time and milestone count versus baseline methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deterministically identifies narrow passages using occupancy grid maps
Increases sampling amount in identified narrow regions
Improves planning time and milestone count performance
🔎 Similar Papers
No similar papers found.
