The field of multi-agent motion planning and collision avoidance is rapidly advancing, with a focus on developing innovative solutions to enable safe and efficient navigation in complex environments. Recent research has explored the use of mixed-integer linear programming, distributed optimal graph control, and model predictive control to address the challenges of multi-agent motion planning. These approaches have shown promising results in improving safety and performance, and have the potential to be applied in a variety of real-world scenarios, such as swarms of drones or mobile robots. Notable papers in this area include:

• A MILP-Based Solution to Multi-Agent Motion Planning and Collision Avoidance, which proposes a novel formulation that reduces binary variables exponentially compared to naive formulations.
• Learning Distributed Safe Multi-Agent Navigation via Infinite-Horizon Optimal Graph Control, which develops a novel Hamilton-Jacobi-Bellman-based learning framework to approximate the optimal solution.