pdf | abstract | bibtex | arXiv | code

Multi-Agent Motion Planning (MAMP) is a problem that seeks collision-free dynamically-feasible trajectories for multiple moving agents in a known environment while minimizing their travel time. MAMP is closely related to the well-studied Multi-Agent Path-Finding (MAPF) problem. Recently, MAPF methods have achieved great success in finding collision-free paths for a substantial number of agents. However, those methods often overlook the kinodynamic constraints of the agents, assuming instantaneous movement, which limits their practicality and realism. In this paper, we present a three-level MAPF-based planner called PSB to address the challenges posed by MAMP. PSB fully considers the kinodynamic capability of the agents and produces solutions with smooth speed profiles that can be directly executed by the controller. Empirically, we evaluate PSB within the domains of traffic intersection coordination for autonomous vehicles and obstacle-rich grid map navigation for mobile robots. PSB shows up to 49.79% improvements in solution cost compared to existing methods.

    @article{YanRAL24,
        author    = {Jingtian Yan and Jiaoyang Li},
        title     = {Multi-Agent Motion Planning With Bézier Curve Optimization Under Kinodynamic Constraints},
        journal   = {IEEE Robotics and Automation Letters},
        doi       = {https://doi.org/10.1109/LRA.2024.3363543},
        year      = {2024}
      }

pdf | abstract | bibtex | arXiv | video

Multi-agent exploration of a bounded 3D environment with the unknown initial poses of agents is a challenging problem. It requires both quickly exploring the environments and robustly merging the sub-maps built by the agents. Most existing exploration strategies directly merge two sub-maps built by different agents when a single frame observation is matched, which can lead to incorrect merging due to the false-positive detection of the overlap and is thus not robust. In the meanwhile, some recent place recognition methods use sequence matching for robust data association. However, naively applying these sequence matching methods to multi-agent exploration may require one agent to repeat a large amount of another agent's history trajectory so that a sequence of matched observation can be established, which reduces the overall exploration time efficiency. To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an adaptive manner based on the quality indicator of the sub-map merging process. Additionally, our approach extends the recent single-agent hierarchical exploration strategy to multiple agents in a cooperative manner for agents whose sub-maps are merged, to improve exploration efficiency. Our experiments show that our approach is up to 50% more efficient than the baselines while merging sub-maps robustly.

@ARTICLE{yan2023mui-tare,
author={Yan, Jingtian and Lin, Xingqiao and Ren, Zhongqiang and Zhao, Shiqi and Yu, Jieqiong and Cao, Chao and Yin, Peng and Zhang, Ji and Scherer, Sebastian},\n journal={IEEE Robotics and Automation Letters}, title={MUI-TARE: Cooperative Multi-Agent Exploration With Unknown Initial Position}, year={2023}, volume={8}, number={7}, pages={4299-4306}, doi={10.1109/LRA.2023.3281262}}

    @ARTICLE{yan2023mui-tare,
        author={Yan, Jingtian and Lin, Xingqiao and Ren, Zhongqiang and Zhao, Shiqi and Yu, Jieqiong and Cao, Chao and Yin, Peng and Zhang, Ji and Scherer, Sebastian},\n
        journal={IEEE Robotics and Automation Letters}, 
        title={MUI-TARE: Cooperative Multi-Agent Exploration With Unknown Initial Position}, 
        year={2023},
        volume={8},
        number={7},
        pages={4299-4306},
        doi={10.1109/LRA.2023.3281262}}

Background | code | video

Background | code | video

The project is designed for enhancing system development and robot deployment for ground-based autonomous navigation and exploration using the NVIDIA Isaac-sim. It encompasses autonomous navigation modules such as collision avoidance, terrain traversability analysis, waypoint following, and more, along with a suite of visualization tools. This project utilizes the hospital environment and Carter robot from the Isaac example files.