Collision Avoidance Constraint

Collision avoidance constraints are crucial in robotics and autonomous systems, aiming to ensure safe and efficient navigation by preventing collisions between moving agents and obstacles. Current research focuses on developing computationally efficient and robust constraint formulations, often employing optimization-based methods like model predictive control (MPC) and alternating direction method of multipliers (ADMM), with various approaches leveraging convex approximations of non-convex constraints or probabilistic methods to handle uncertainty. These advancements are significant for improving the safety and reliability of autonomous systems in diverse applications, from multi-robot coordination to self-driving cars and UAV navigation.