SwarmPRM: Probabilistic Roadmap Motion Planning for Large-Scale Swarm Robotic Systems
<|reference_start|>SwarmPRM: Probabilistic Roadmap Motion Planning for Large-Scale Swarm Robotic Systems: Large-scale swarm robotic systems consisting of numerous cooperative agents show considerable promise for performing autonomous tasks across various sectors. Nonetheless, traditional motion planning approaches often face a trade-off between scalability and solution quality due to the exponential growth of the joint state space of robots. In response, this work proposes SwarmPRM, a hierarchical, scalable, computationally efficient, and risk-aware sampling-based motion planning approach for large-scale swarm robots. SwarmPRM utilizes a Gaussian Mixture Model (GMM) to represent the swarm's macroscopic state and constructs a Probabilistic Roadmap in Gaussian space, referred to as the Gaussian roadmap, to generate a transport trajectory of GMM. This trajectory is then followed by each robot at the microscopic stage. To enhance trajectory safety, SwarmPRM incorporates the conditional value-at-risk (CVaR) in the collision checking process to impart the property of risk awareness to the constructed Gaussian roadmap. SwarmPRM then crafts a linear programming formulation to compute the optimal GMM transport trajectory within this roadmap. Extensive simulations demonstrate that SwarmPRM outperforms state-of-the-art methods in computational efficiency, scalability, and trajectory quality while offering the capability to adjust the risk tolerance of generated trajectories.<|reference_end|>
@article{hu2024swarmprm:,
    title={SwarmPRM: Probabilistic Roadmap Motion Planning for Large-Scale Swarm
  Robotic Systems},
    author={Yunze Hu, Xuru Yang, Kangjie Zhou, Qinghang Liu, Kang Ding, Han Gao,
  Pingping Zhu, and Chang Liu},
    journal={arXiv preprint arXiv:2402.16699},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16699},
    primaryClass={cs.RO}
}