Feasible Trajectory

Feasible trajectory planning focuses on generating paths for robots and autonomous systems that are both collision-free and dynamically achievable, considering constraints like actuator limits and environmental factors. Current research emphasizes efficient algorithms, such as model predictive control (MPC), particle swarm optimization, and graph search methods often combined with trajectory optimization techniques, to handle complex scenarios and high-dimensional state spaces. These advancements are crucial for enabling safe and efficient operation of robots in real-world applications, ranging from autonomous driving and aerial robotics to humanoid locomotion and industrial manipulation.