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

By Xiaoxia Zhou, Zhepei Wang, Chao Xu, Fei Gao. IEEE Robotics and Automation Letters, 2020

515 citations recorded by the source corpus.

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

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6fd8ff3bfee9ba2b57eea07780da868964a47486

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

  1. Zhepei Wang
  2. Fei Gao
  3. Chao Xu
  4. Xiaoxia Zhou

In Computer Vision and Pattern Recognition

  1. Fei Gao
  2. Chao Xu
  3. Dacheng Tao

Citations in the corpus