Xia Li This email address is being protected from spambots. You need JavaScript enabled to view it.1

1College of Intelligent Manufacturing Engineers, Hefei College of Finance & Economics, Anhui, China,230601


Received: May 19, 2022
Accepted: June 30, 2022
Publication Date: August 16, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202305_26(5).0008  


Aiming at the existing problems of multiple sensors, a multi-sensor cooperative positioning technology based on dual neural networks is proposed. The established multi-sensor joint indoor positioning model introduces inertial sensor data information on the basis of theWi-Fi positioning model. This article uses Gated Recurrent Unit (GRU) to establish a motion trajectory model, which improves the accuracy of indoor positioning and anti-interference ability, so that it can still maintain good positioning accuracy in a complex indoor environment. The basic modeling of UAV trajectory planning is studied. We analyze the physical performance and planning environment of the UAV, and transform the problem into a mathematical function. In the realization of the planning algorithm, the road sign guidance method is used to solve the local trap problem of the velocity vector field. We research on the UAV constraint fusion method, and use the "detection step method" to solve the UAV minimum turning radius constraint fusion problem. By setting the distance between the wall-climbing robot and the UAV formation relationship and introducing the speed of avoiding other UAVs, the trajectory collision-free constraint fusion is realized. On this basis, two coordination mechanisms of temporary target selection and detection step adjustment are designed to realize the coordination of the trajectory of the wall-climbing robot and the UAV in different scenarios. We study environment update methods and adopt partial update strategies to realize the processing of planning space update, task update, and threat update, so as to ensure that the algorithm can be applied to a dynamically changing environment. The design of the planning algorithm was completed, the track evaluation index was proposed, and the simulation verification software was developed to complete the verification of the algorithm’s feasibility, rationality, completeness, and rapidity. The effects of adding threats and deleting threats during the planning process on the coordinated trajectory planning of wall-climbing robots and UAVs are studied. In both cases, the wall-climbing robot and the UAV can avoid the threat area and reach their respective target points in coordination. It can be seen that the local update mechanism of the environment can quickly and effectively respond to the dynamic change of the threat, which not only ensures the speed of the algorithm. It also ensures that wall-climbing robots and drones can safely avoid dynamic threats, effectively realizing the collaboration allowed by the environment.

Keywords: wall-climbing robot; unmanned aerial vehicle; collaborative constraint; multi-sensor


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