The field of robotics is witnessing significant advancements in multi-robot systems and motion planning. Researchers are developing innovative approaches to optimize task allocation, motion planning, and coverage control for multi-robot systems. One notable direction is the use of machine learning and optimization techniques to improve the efficiency and effectiveness of multi-robot systems. Another area of focus is the development of algorithms and protocols that enable heterogeneous robots to collaborate and move efficiently in shared environments. Noteworthy papers in this area include: Generalizing Multi-Objective Search via Objective-Aggregation Functions, which proposes a generalized problem formulation that optimizes solution objectives via aggregation functions of hidden objectives. SRMP: Search-Based Robot Motion Planning Library, which introduces a new software framework tailored for robotic manipulation that generates consistent and reliable trajectories. Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks, which shows how Conflict-Based Search can be used as a protocol to enable efficient collision-free movements between algorithmically heterogeneous agents.