Static Obstacle

Static obstacle avoidance is a crucial problem in robotics and autonomous systems, focusing on generating safe and efficient trajectories that circumvent stationary obstructions. Current research emphasizes learning-based approaches, such as reinforcement learning and imitation learning, often coupled with optimization techniques like sum-of-squares or nonlinear programming to generate collision-free paths. These methods are being applied across diverse platforms, from autonomous vehicles and robotic manipulators to quadrotor swarms, improving safety and efficiency in navigation and manipulation tasks. The development of robust and computationally efficient algorithms for static obstacle avoidance has significant implications for the safety and reliability of autonomous systems in various real-world applications.