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.

中文翻译：

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EGO-Planner：用于四旋翼飞行器的无 ESDF、基于梯度的局部规划器


基于梯度的规划器广泛用于四旋翼局部规划，其中欧几里德符号距离场（ESDF）对于评估梯度大小和方向至关重要。然而，计算这样的场具有很多冗余，因为轨迹优化过程仅覆盖 ESDF 更新范围的非常有限的子空间。在这封信中，提出了一种无 ESDF 的基于梯度的规划框架，该框架显着减少了计算时间。主要改进是通过将碰撞轨迹与无碰撞引导路径进行比较来制定罚函数中的碰撞项。仅当轨迹遇到新的障碍物时才会存储由此产生的障碍物信息，使得规划器仅提取必要的障碍物信息。然后，如果违反动态可行性，我们会延长时间分配。引入各向异性曲线拟合算法来调整轨迹的高阶导数，同时保持原始形状。基准比较和实际实验验证了其稳健性和高性能。源代码以 ros 包的形式发布。