ICUAS'23 Paper Abstract

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Paper WeA3.2

Wang, Xiaoyu (Tsinghua University), Huang, Kangyao (Tsinghua University), Zhang, Xinyu (Tsinghua University), Sun, Honglin (Tsinghua University), Liu, Wenzhuo (Tsinghua University), Liu, Huaping (Tsinghua University), Li, Jun (Tsinghua University), Lu, Pingping (University of Michigan)

Path Planning for Air-Ground Amphibious Robot Considering Modal Switching Point Optimization

Scheduled for presentation during the Regular Session "Path Planning I" (WeA3), Wednesday, June 7, 2023, 11:20−11:40, Room 464

2023 International Conference on Unmanned Aircraft Systems (ICUAS), June 6-9, 2023, Lazarski University, Warsaw, Poland

This information is tentative and subject to change. Compiled on March 29, 2024

Keywords Path Planning, Control Architectures, Reliability of UAS

Abstract

An innovative sort of mobility platform that can both drive and fly is the air-ground robot. The need for an agile flight cannot be satisfied by traditional path planning techniques for air-ground robots. Prior studies had mostly focused on improving the energy efficiency of paths, seldom taking the seeking speed and optimizing take-off and landing places into account. A robot for the field application environ- ment was proposed, and a lightweight global spatial planning technique for the robot based on the graph-search algorithm taking mode switching point optimization into account, with an emphasis on energy efficiency, searching speed, and the viability of real deployment. The fundamental concept is to lower the computational burden by employing an interchangeable search approach that combines planar and spatial search. Furthermore, to safeguard the health of the power battery and the integrity of the mission execution, a trap escape approach was also provided. Simulations are run to test the effectiveness of the suggested model based on the field DEM map. The simulation results show that our technology is capable of producing finished, plausible 3D paths with a high degree of believability. Additionally, the mode-switching point optimization method efficiently identifies additional acceptable places for mode switching, and the improved paths use less time and energy.

 

 

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