# EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors

arXiv: https://arxiv.org/abs/2008.08835 · HTML: https://mlsanity.com/paper/2008.08835/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 30 of 55)

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- [Embodied AI: From LLMs to World Models](https://mlsanity.com/paper/2509.20021/) (2025)
- [VLA-AN: An Efficient and Onboard Vision-Language-Action Framework for Aerial Navigation in Complex Environments](https://mlsanity.com/paper/2512.15258/) (2025)
- [DPMPC-Planner: A real-time UAV trajectory planning framework for complex static environments with dynamic obstacles](https://mlsanity.com/paper/2109.07024/) (2021)
- [Learning Speed Adaptation for Flight in Clutter](https://mlsanity.com/paper/2403.04586/) (2024)
- [A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with Dynamic Obstacle Trajectory Prediction and Its Application with LLMs](https://mlsanity.com/paper/2311.12893/) (2023)
- [Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy](https://mlsanity.com/paper/2109.06479/) (2021)
- [NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning](https://mlsanity.com/paper/2403.06828/) (2024)
- [Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization](https://mlsanity.com/paper/2209.07003/) (2022)
- [NavDreamer: Video Models as Zero-Shot 3D Navigators](https://mlsanity.com/paper/2602.09765/) (2026)
- [Flying on Point Clouds with Reinforcement Learning](https://mlsanity.com/paper/2503.00496/) (2025)
- [Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators](https://mlsanity.com/paper/2104.03663/) (2021)
- [BatMobility: Towards Flying Without Seeing for Autonomous Drones](https://mlsanity.com/paper/2307.11518/) (2023)
- [Fly0: Persistent Metric Anchoring for Zero-Shot Aerial Vision-Language Navigation](https://mlsanity.com/paper/2602.15875/) (2026)
- [Depth Transfer: Learning to See Like a Simulator for Real-World Drone Navigation](https://mlsanity.com/paper/2505.12428/) (2025)
- [FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation](https://mlsanity.com/paper/2505.20783/) (2025)
- [AirDreamer: Generalist Drone Navigation with World Models](https://mlsanity.com/paper/2606.03252/) (2026)
- [Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control](https://mlsanity.com/paper/2210.10639/) (2022)
- [Certificated Actor-Critic: Hierarchical Reinforcement Learning with Control Barrier Functions for Safe Navigation](https://mlsanity.com/paper/2501.17424/) (2025)
- [Efficient Navigation in Unknown Indoor Environments with Vision-Language Models](https://mlsanity.com/paper/2510.04991/) (2025)
- [Fly360: Omnidirectional Obstacle Avoidance within Drone View](https://mlsanity.com/paper/2603.06573/) (2026)
- [ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception](https://mlsanity.com/paper/2607.10180/) (2026)
- [OppLoD: the Opponency based Looming Detector, Model Extension of Looming Sensitivity from LGMD to LPLC2](https://mlsanity.com/paper/2302.10284/) (2023)
- [Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination](https://mlsanity.com/paper/2403.17231/) (2024)
- [Active Human Pose Estimation via an Autonomous UAV Agent](https://mlsanity.com/paper/2407.01811/) (2024)
- [Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone](https://mlsanity.com/paper/2501.12073/) (2025)

## Abstract (arXiv)

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 paper, an ESDF-free gradient-based planning framework is proposed, which significantly reduces computation time. The main improvement is that the collision term in the 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.