EGO-Planner: An ESDF-Free Gradient-Based Local Planner for Quadrotors

Gradient-based planners are widely used for quadrotor local planning, in which a Euclidean Signed Distance Field (ESDF) is crucial evaluating gradient magnitude and direction. Nevertheless, computing such field has much redundancy since the trajectory optimization procedure only covers very limited subspace of ESDF updating range. In this letter, an ESDF-free gradient-based planning framework proposed, significantly reduces computation time. The main improvement that collision term penalty function formulated by comparing colliding with collision-free guiding path. resulting obstacle information will be stored if hits new obstacles, making planner extract necessary information. Then, we lengthen time allocation dynamical feasibility violated. An anisotropic curve fitting algorithm introduced to adjust higher order derivatives while maintaining original shape. Benchmark comparisons real-world experiments verify its robustness high-performance. source code released as ros packages.