The field of motion planning and navigation is moving towards more efficient and robust methods for handling complex environments and multiple agents. Recent developments have focused on improving the scalability and reliability of path planning algorithms, particularly in situations where there are many obstacles or agents. One key area of innovation is the integration of new techniques, such as stochastic restarts and probabilistic gap planning, into existing frameworks to enhance their performance and adaptability. These advances have the potential to improve the safety and efficiency of autonomous systems operating in crowded or dynamic environments. Noteworthy papers include:

• CSC-MPPI, which proposes a novel constrained formulation of MPPI that enhances trajectory optimization and guarantees constraint satisfaction.
• A Scalable Post-Processing Pipeline for Large-Scale Free-Space Multi-Agent Path Planning with PiBT, which presents a hybrid planning framework that combines Priority Inheritance with Backtracking and a novel safety-aware path smoothing method to achieve near-optimal trajectories in sparse domains.
• Faster Motion Planning via Restarts, which applies stochastic restart techniques to speed up Las Vegas algorithms and demonstrates dramatic speedups in practice.
• Finding the Easy Way Through -- the Probabilistic Gap Planner for Social Robot Navigation, which proposes a conflict avoidance planner that biases the short-term CCA planner to head towards gaps in the crowd and improves the agents' performance in crowded environments.
• Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations, which incorporates uncertain future motions modelled with potentially non-Gaussian stochastic predictions and efficiently approximates the joint Collision Probability among multiple dynamic obstacles in real-time.