Robot Motion Planning

Robot motion planning focuses on generating safe and efficient trajectories for robots navigating complex environments, often incorporating constraints like obstacles and dynamic elements. Current research emphasizes integrating machine learning, particularly reinforcement learning and neural networks, with classical sampling-based and search-based algorithms like RRT and A*, to improve efficiency, robustness, and adaptability in high-dimensional spaces. This field is crucial for advancing autonomous robotics, enabling safer and more effective human-robot collaboration in industrial and domestic settings, and driving progress in areas like automated driving and warehouse automation.