Risk-Aware Planner for Quadrotor in Cluttered and Dynamic Environments

Autonomous quadrotors face significant challenges in navigating through complex environments due to dynamic obstacles. Existing trajectory planning methods typically rely on simplified motion assumptions and struggle to account for the uncertainties introduced by dynamic agents, leading to overly conservative or unsafe paths. To address this issue, we propose a risk-aware planner that integrates probabilistic risk assessment into motion planning, which incorporates dynamic constraints to enable safer and more efficient navigation. Our approach first generates a risk map by combining a probabilistic representation of dynamic obstacles with a distance-based static risk model, offering a more comprehensive environmental risk assessment. Based on this map, we introduce a kinodynamic A* planner that generates an initial path utilizing risk-based heuristics, which is then optimized to minimize risk while ensuring smoothness and feasibility. Simulation experiments demonstrate that our method allows quadrotors to navigate dynamic environments more safely and efficiently.