Minimum-time trajectory generation for quadrotors in constrained environments
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Abstract (arXiv)
In this paper, we present a novel strategy to compute minimum-time trajectories for quadrotors in constrained environments. In particular, we consider the motion in a given flying region with obstacles and take into account the physical limitations of the vehicle. Instead of approaching the optimization problem in its standard time-parameterized formulation, the proposed strategy is based on an appealing re-formulation. Transverse coordinates, expressing the distance from a frame path, are used to parameterise the vehicle position and a spatial parameter is used as independent variable. This re-formulation allows us to (i) obtain a fixed horizon problem and (ii) easily formulate (fairly complex) position constraints. The effectiveness of the proposed strategy is proven by numerical computations on two different illustrative scenarios. Moreover, the optimal trajectory generated in the second scenario is experimentally executed with a real nano-quadrotor in order to show its feasibility.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 72
- 2021Autonomous Drone Racing with Deep Reinforcement Learningcited by 51
- 2021Time-Optimal Planning for Quadrotor Waypoint Flightcited by 14
- 2022SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problemscited by 9
- 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning17 Oct 2023
- 2022SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems14 Jan 2022
- 2021Time-Optimal Planning for Quadrotor Waypoint Flight10 Aug 2021
- 2021Autonomous Drone Racing with Deep Reinforcement Learning15 Mar 2021