The field of multi-robot navigation and collision avoidance is moving towards more decentralized and adaptive approaches. Researchers are exploring hybrid frameworks that combine the benefits of decentralized path planning with centralized conflict resolution, allowing for more efficient and scalable solutions. Decentralized swarm intelligence strategies are also being developed to tackle complex problems such as avoiding jackknifing and mutual collisions in heavy articulated vehicles. Additionally, there is a focus on enhancing existing algorithms and frameworks, such as PIBT and MPPI, to improve their performance and efficiency in various scenarios. Noteworthy papers in this area include:

• Virtual Traffic Lights for Multi-Robot Navigation, which presents a novel hybrid framework for multi-robot coordination.
• CoRL-MPPI, which introduces a fusion of Cooperative Reinforcement Learning and MPPI for efficient and provably-safe multi-robot collision avoidance.