Planning collision-free paths for a large group of agents is a challenging problem in many real-world applications. While recent advances in Multi-Agent Path Finding (MAPF) have shown promising progress, standard MAPF planners continue to rely on simplified kinodynamic models, preventing agents from directly following the generated MAPF plan. To bridge this gap, we propose kinodynamic Temporal Plan Graph planning (kTPG), a multi-agent speed optimization algorithm that efficiently refines a MAPF plan into a set of kinodynamically feasible speed profiles. We further incorporate execution timing uncertainty models and provide deterministic guarantees under bounded uncertainty models and probabilistic guarantees under stochastic models. Building on kTPG, we propose Windowed kTPG (WinkTPG), a MAPF execution framework that incrementally refines MAPF plans using a window-based mechanism, dynamically incorporating agent information during execution to reduce uncertainty. Experiments show that WinkTPG can generate speed profiles for up to 1,000 agents within 1 second and improves solution quality by up to 51.7% over existing MAPF execution methods. We further validate WinkTPG in high-fidelity physics-based simulation and on real-world robots.

Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation. In many of these environments, differential drive robots are commonly used. These robots have a kinodynamic model that allows only in-place rotation and movement along their current orientation, subject to speed and acceleration limits. However, existing Multi-Agent Path Finding (MAPF)-based methods often use simplified models for robot kinodynamics, which limits their practicality and realism. In this paper, we introduce a three-level framework called MASS to address these challenges. MASS combines MAPF-based methods with our proposed stationary state search planner to generate high-quality kinodynamically-feasible plans. We further extend MASS using an adaptive window mechanism to address the lifelong MAMP problem. Empirically, we tested our methods on the single-shot grid map domain and the lifelong warehouse domain. Our method shows up to 400% improvements in terms of throughput compared to existing methods.

Multi-Agent Motion Planning (MAMP) finds various real-world applications in fields such as traffic management, airport operations, and warehouse automation. This work primarily focuses on its application in large-scale automated warehouses. Recently, Multi-Agent Path-Finding (MAPF) methods have achieved great success in finding collision-free paths for hundreds of agents within automated warehouse settings. However, these methods often use a simplified assumption about the robot dynamics, which limits their practicality and realism. In this paper, we introduce a three-level MAMP framework called PSS which incorporates the kinodynamic constraints of the robots. PSS combines MAPF-based methods with Stationary Safe Interval Path Planner (SSIPP) to generate high-quality kinodynamically-feasible solutions. Our method shows significant improvements in terms of scalability and solution quality compared to existing methods.

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. In this paper, we introduce a three-level planner called PSB that combines search-based and optimization-based techniques to address the challenges posed by MAMP. PSB fully considers the kinodynamic capability of the agents and produces solutions with smooth speed profiles. Empirically, we evaluate PSB within the domain of obstacle-rich grid map navigation for mobile robots. PSB shows up to 49.79% improvements in solution cost compared to existing methods while achieving significant improvement in scalability.