Kinematic Constraint

Kinematic constraint research focuses on planning and controlling robot movements while adhering to physical limitations like joint angles, actuator limits, and collision avoidance. Current research emphasizes efficient algorithms, such as sampling-based planners (e.g., RRT, PRM) and model predictive control (MPC), often enhanced by parallelization techniques (e.g., GPU acceleration) to achieve real-time performance in complex environments. These advancements are crucial for enabling safe and effective robot navigation and manipulation in diverse applications, from collaborative robotics and autonomous driving to minimally invasive surgery and humanoid locomotion. The development of robust and computationally efficient methods for handling kinematic constraints is a key driver of progress in advanced robotics.