Collision Constraint

Collision constraint research focuses on efficiently and reliably generating collision-free trajectories for robots and autonomous vehicles, addressing challenges in real-time control and complex environments. Current approaches leverage optimization techniques, including model predictive control, barrier functions (explicit and implicit), and alternating direction method of multipliers (ADMM), often incorporating model reduction or constraint tightening to improve computational speed and robustness. These advancements are crucial for enabling safe and efficient operation of robots in diverse applications, from manipulation and legged locomotion to autonomous driving and multi-robot coordination.