MINER-RRT: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments

Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, w...

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Vydáno v:IEEE transactions on automation science and engineering Ročník 22; s. 10973 - 10985
Hlavní autoři: Wang, Pengyu, Tang, Jiawei, Wang Lin, Hin, Zhang, Fan, Wang, Chaoqun, Wang, Jiankun, Shi, Ling, Meng, Max Q.-H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
deep neural network
Heuristic algorithms
Planning
Polynomials
Quadrotors
Resource management
Robot trajectory planning
sampling-based algorithm
Splines (mathematics)
Three-dimensional displays
Trajectory
Trajectory optimization
Trajectory planning
ISSN:1545-5955, 1558-3783
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Shrnutí:Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor's differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments ( https://youtu.be/fXuuMRX19q0 ). Note to Practitioners-The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in real scenarios and empower quadrotors for complex autonomous tasks in the future.
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2025.3531504