🤖 AI Summary
To address the challenges of robotic navigation in unknown, densely cluttered environments—specifically excessive conservatism, myopia, and entrapment in dead ends—this paper proposes a synergistic framework integrating real-time topological roadmap construction with Dynamic System Modulation (DSM), grounded in a star-shaped spatial representation. Methodologically, we introduce a novel piecewise-polynomial star-shaped region modeling technique coupled with an incremental connectivity graph update mechanism; design a dead-zone self-repairing graph evolution strategy; and develop a reactive DSM controller tailored for star-shaped region intersections, enabling closed-loop online perception–planning–control via LiDAR/depth data fusion. Our contributions include significantly enhanced obstacle avoidance robustness and path optimality, while mitigating conservative behavior. Extensive evaluations in simulation and on real robotic platforms demonstrate state-of-the-art performance in task success rate, path efficiency, and real-time responsiveness.
📝 Abstract
Compared to conventional decomposition methods that use ellipses or polygons to represent free space, starshaped representation can better capture the natural distribution of sensor data, thereby exploiting a larger portion of traversable space. This paper introduces a novel motion planning and control framework for navigating robots in unknown and cluttered environments using a dynamically constructed starshaped roadmap. Our approach generates a starshaped representation of the surrounding free space from real-time sensor data using piece-wise polynomials. Additionally, an incremental roadmap maintaining the connectivity information is constructed, and a searching algorithm efficiently selects short-term goals on this roadmap. Importantly, this framework addresses dead-end situations with a graph updating mechanism. To ensure safe and efficient movement within the starshaped roadmap, we propose a reactive controller based on Dynamic System Modulation (DSM). This controller facilitates smooth motion within starshaped regions and their intersections, avoiding conservative and short-sighted behaviors and allowing the system to handle intricate obstacle configurations in unknown and cluttered environments. Comprehensive evaluations in both simulations and real-world experiments show that the proposed method achieves higher success rates and reduced travel times compared to other methods. It effectively manages intricate obstacle configurations, avoiding conservative and myopic behaviors.