mlsanityData to 14 Oct 2026

Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning

arXiv 2409.16720 ↗First posted 25 Sep 2024cs.RO cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

Recent innovations in autonomous drones have facilitated time-optimal flight in single-drone configurations, and enhanced maneuverability in multi-drone systems by applying optimal control and learning-based methods. However, few studies have achieved time-optimal motion planning for multi-drone systems, particularly during highly agile maneuvers or in dynamic scenarios. This paper presents a decentralized policy network using multi-agent reinforcement learning for time-optimal multi-drone flight. To strike a balance between flight efficiency and collision avoidance, we introduce a soft collision-free mechanism inspired by optimization-based methods. By customizing PPO in a centralized training, decentralized execution (CTDE) fashion, we unlock higher efficiency and stability in training while ensuring lightweight implementation. Extensive simulations show that, despite slight performance trade-offs compared to single-drone systems, our multi-drone approach maintains near-time-optimal performance with a low collision rate. Real-world experiments validate our method, with two quadrotors using the same network as in simulation achieving a maximum speed of 13.65 m/s and a maximum body rate of 13.4 rad/s in a 5.5 m * 5.5 m * 2.0 m space across various tracks, relying entirely on onboard computation.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

22 of 33 references.

References · 22

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  4. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 294
  5. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  6. 2021Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharingcited by 87
  7. 2017Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learningcited by 83
  8. 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 77
  9. 2024Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physicscited by 45
  10. 2017Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectoriescited by 32
  11. 2021Time-Optimal Planning for Quadrotor Waypoint Flightcited by 23
  12. 2020MADER: Trajectory Planner in Multi-Agent and Dynamic Environmentscited by 21
10 more
  1. 2021Model Predictive Contouring Control for Time-Optimal Quadrotor Flightcited by 21
  2. 2021Geometrically Constrained Trajectory Optimization for Multicopterscited by 19
  3. 2023End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flightcited by 17
  4. 2024Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidancecited by 14
  5. 2022Time-Optimal Online Replanning for Agile Quadrotor Flightcited by 8
  6. 2023Time-Optimal Gate-Traversing Planner for Autonomous Drone Racingcited by 7
  7. 2023AMSwarm: An Alternating Minimization Approach for Safe Motion Planning of Quadrotor Swarms in Cluttered Environmentscited by 5
  8. 2023Euclidean and non-Euclidean Trajectory Optimization Approaches for Quadrotor Racingcited by 2
  9. 2023Aggressive Trajectory Generation for A Swarm of Autonomous Racing Dronescited by 1
  10. 2023AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Spacecited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Agile Flight Emerges from Multi-Agent Competitive Racingcited by 4
  2. 2025Towards Task-Oriented Flying: Framework, Infrastructure, and Principlescited by 1
  3. 2025FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learningcited by 1
  4. 2026Agile Interception of a Flying Target using Competitive Reinforcement Learningcited by 1
  5. 2026MAVEN: A Meta-Reinforcement Learning Framework for Varying-Dynamics Expertise in Agile Quadrotor Maneuverscited by 0
  6. 2026ASTER: Attitude-aware Suspended-payload Quadrotor Traversal via Efficient Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Intercepting an Agile Target with Net-Carrying Drones using Competitive Multi-Agent Reinforcement Learning ↗
  2. 2024Time-optimal Flight in Cluttered Environments via Safe Reinforcement Learning
  3. 2025Simultaneous learning of state-to-state minimum-time planning and control ↗
  4. 2025Multi-Agent Deep Reinforcement Learning for Collaborative UAV Relay Networks under Jamming Atatcks
  5. 2025Reinforcement Learning for Decision-Level Interception Prioritization in Drone Swarm Defense
  6. 2025Curriculum-Based Iterative Self-Play for Scalable Multi-Drone Racing
  7. 2026Bridging Performance and Generalization in Reinforcement Learning for Agile Flight
  8. 2025Reactive Aerobatic Flight via Reinforcement Learning