Sampling-Based Real-Time Motion Planning under State Uncertainty for Autonomous Micro-Aerial Vehicles in GPS-Denied EnvironmentsReport as inadecuate




Sampling-Based Real-Time Motion Planning under State Uncertainty for Autonomous Micro-Aerial Vehicles in GPS-Denied Environments - Download this document for free, or read online. Document in PDF available to download.

1

Department of Automation, Tsinghua University, Bejing 100084, China

2

National Key Laboratory on Flight Vehicle Control Integrated Technology, Flight Automatic Control Research Institute, Xian 710065, China





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Abstract This paper presents a real-time motion planning approach for autonomous vehicles with complex dynamics and state uncertainty. The approach is motivated by the motion planning problem for autonomous vehicles navigating in GPS-denied dynamic environments, which involves non-linear and-or non-holonomic vehicle dynamics, incomplete state estimates, and constraints imposed by uncertain and cluttered environments. To address the above motion planning problem, we propose an extension of the closed-loop rapid belief trees, the closed-loop random belief trees CL-RBT, which incorporates predictions of the position estimation uncertainty, using a factored form of the covariance provided by the Kalman filter-based estimator. The proposed motion planner operates by incrementally constructing a tree of dynamically feasible trajectories using the closed-loop prediction, while selecting candidate paths with low uncertainty using efficient covariance update and propagation. The algorithm can operate in real-time, continuously providing the controller with feasible paths for execution, enabling the vehicle to account for dynamic and uncertain environments. Simulation results demonstrate that the proposed approach can generate feasible trajectories that reduce the state estimation uncertainty, while handling complex vehicle dynamics and environment constraints. View Full-Text

Keywords: motion planning; micro-aerial vehicles; rapidly exploring random trees RRT; state estimation uncertainty motion planning; micro-aerial vehicles; rapidly exploring random trees RRT; state estimation uncertainty





Author: Dachuan Li 1,* , Qing Li 1, Nong Cheng 1,2 and Jingyan Song 1

Source: http://mdpi.com/



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