The field of autonomous systems is rapidly advancing, with a focus on developing more efficient and safe motion planning and control algorithms. Recent research has explored the use of model predictive control, nonlinear model predictive control, and robust permissive controller synthesis to improve the performance of autonomous systems in dynamic environments. The development of novel frameworks and algorithms has enabled the creation of more accurate and adaptive path planning and obstacle avoidance capabilities, which are essential for ensuring safety in autonomous vehicles and other applications. Notable papers in this area include: Efficient Optimal Path Planning in Dynamic Environments Using Koopman MPC, which presents a data-driven model predictive control framework for mobile robots navigating in dynamic environments. Real-Time Nonlinear Model Predictive Control of Heavy-Duty Skid-Steered Mobile Platform for Trajectory Tracking Tasks, which proposes a multiple-shooting nonlinear model-predictive control framework for real-time optimal controlling of a heavy-duty skid-steered mobile platform. R3R: Decentralized Multi-Agent Collision Avoidance with Infinite-Horizon Safety, which presents a decentralized and asynchronous framework for multi-agent motion planning under distance-based communication constraints with infinite-horizon safety guarantees. HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving, which combines methods for multi-agent behavior prediction, deep reinforcement learning, and approximated online POMDP planning to solve the collision-free navigation problem for self-driving cars in partially observable traffic environments.