Generic Motion Primitives-based Safe Motion Planner under Uncertainty for Autonomous Navigation in Cluttered Environments

The safe and efficient operation of autonomous vehicles in cluttered environments with uncertainties remains an ongoing research challenge. Current planning algorithms often use simplified models that do not fully exploit the system's dynamic potential and can lead to collisions, or violate the constraints. To address this issue, we propose a novel moving horizon planning and control approach using the concept of motion primitives. Our approach formulates the planning problem as an optimization over generic motion primitives sets and incorporates ideas from robust predictive control for collision avoidance and handling uncertainties. The proposed approach reduces computational complexity while improving the feasibility of the generated path. We reformulate the problem as a mixed in-teger linear optimization program and present simulation results demonstrating that our approach maximizes vehicle functionality while avoiding obstacles. The proposed framework is tested on simulation scenarios. The results demonstrate its effectiveness and robustness in generating safe, efficient, and reliable trajecto-ries for autonomous systems operating in uncertain environments, highlighting its potential for real-world implementation.