Online Motion Planning

Online motion planning focuses on enabling robots to navigate and manipulate objects in real-time, dynamically adapting to changing environments and uncertainties. Current research emphasizes efficient algorithms like model predictive control (MPC) and sampling-based methods, often incorporating adaptive map representations (e.g., OctoMaps) and chance-constrained optimization to handle stochasticity and safety. These advancements are crucial for improving the robustness and reliability of autonomous systems in complex, unpredictable scenarios, with applications ranging from industrial robotics and autonomous driving to search and rescue operations.