Obstacle Avoidance Constraint

Obstacle avoidance constraints are crucial for safe and efficient operation of autonomous systems, ranging from robots to self-driving cars and UAVs. Current research focuses on integrating these constraints into various control frameworks, including model predictive control (MPC) and inverse kinematics, often employing optimization techniques like linear programming or nonlinear programming to find feasible trajectories while satisfying constraints such as joint limits and obstacle geometries represented by polytopes or point clouds. These advancements are significantly impacting the development of robust and reliable autonomous systems across diverse applications, enabling real-time navigation in complex and dynamic environments.