mlsanityData to 14 Oct 2026

RESC: A Reinforcement Learning Based Search-to-Control Framework for Quadrotor Local Planning in Dense Environments

arXiv 2408.00275 ↗First posted 1 Aug 2024cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Agile flight in complex environments poses significant challenges to current motion planning methods, as they often fail to fully leverage the quadrotor dynamic potential, leading to performance failures and reduced efficiency during aggressive maneuvers.Existing approaches frequently decouple trajectory optimization from control generation and neglect the dynamics, further limiting their ability to generate aggressive and feasible motions.To address these challenges, we introduce an enhanced Search-to-Control planning framework that integrates visibility path searching with reinforcement learning (RL) control generation, directly accounting for dynamics and bridging the gap between planning and control.Our method first extracts control points from collision-free paths using a proposed heuristic search, which are then refined by an RL policy to generate low-level control commands for the quadrotor controller, utilizing reduced-dimensional obstacle observations for efficient inference with lightweight neural networks.We validate the framework through simulations and real-world experiments, demonstrating improved time efficiency and dynamic maneuverability compared to existing methods, while confirming its robustness and applicability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 20 of its 36 references that are in the map. First posted on arXiv 1 Aug 2024.

Builds on

20 of 36 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight18 Mar

Other references · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016OpenAI Gymcited by 1,736
  3. 2021Learning High-Speed Flight in the Wildcited by 128
  4. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  5. 2021Autonomous Drone Racing with Deep Reinforcement Learningcited by 60
  6. 2020EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotorscited by 55
  7. 2022A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flightcited by 47
  8. 2019Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flightcited by 41
  9. 2020RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flightcited by 23
  10. 2022Learning Perception-Aware Agile Flight in Cluttered Environmentscited by 23
  11. 2021Model Predictive Contouring Control for Time-Optimal Quadrotor Flightcited by 21
  12. 2021Geometrically Constrained Trajectory Optimization for Multicopterscited by 19
7 more
  1. 2023Autonomous Power Line Inspection with Drones via Perception-Aware MPCcited by 17
  2. 2022Learning Minimum-Time Flight in Cluttered Environmentscited by 16
  3. 2020FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environmentscited by 15
  4. 2024Learning Speed Adaptation for Flight in Cluttercited by 14
  5. 2019An Efficient B-spline-Based Kinodynamic Replanning Framework for Quadrotorscited by 5
  6. 2019Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Pathscited by 4
  7. 2024Model Predictive Path Integral Control for Agile Unmanned Aerial Vehiclescited by 3

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026MAVEN: A Meta-Reinforcement Learning Framework for Varying-Dynamics Expertise in Agile Quadrotor Maneuvers
  2. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking
  3. 2025Reactive Aerobatic Flight via Reinforcement Learning
  4. 2026Simple but Stable, Fast and Safe: Achieve End-to-end Control by High-Fidelity Differentiable Simulation
  5. 2025Quadrotor Navigation using Reinforcement Learning with Privileged Information
  6. 2026Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles
  7. 2026Precise Aggressive Aerial Maneuvers with Sensorimotor Policies
  8. 2024Demonstrating Agile Flight from Pixels without State Estimation