使用自主无人机导航和网络服务设计未知地点的障碍物地图

Jitender Tanwar, S. Sharma, M. Mittal
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引用次数: 1

摘要

无人机用于多种目的,包括检查区域,绘制周围环境和救援任务行动。在这些任务中,他们可能会遇到复杂的环境,有多种障碍,尖锐的边缘和死锁。本文的目的是提出一种无人机自主移动所需的避障技术,并动态生成未知地点的障碍物地图。因此,自动移动需要避障技术。无人机的自动化需要复杂的视觉传感器和高计算能力。在这项研究中,提出了一种方法,将两个基本的超声波定向接近传感器放置在无人机的中心,并利用突触可塑性应用神经控制来实现动态避障。采用神经控制方法建立了双神经元间歇系统。突触可塑性用于从不同的角度找到转弯角度并立即记忆,因此它有助于无人机的决策。因此,在未知的狭窄路口和死胡同中,自动机将能够在路线中四处行驶并修改其转弯角度以躲避物体。此外,在路线中检测到障碍物时,通过RESTful Web服务将坐标信息传递给android应用程序,并根据无人机发送的信息生成障碍物地图。在本研究中,成功设计并实现了无人机的自动化,并利用V-REP仿真环境生成了障碍物图。仿真结果表明,无人机能够有效地移动和绕过障碍物,利用web服务对无人机进行动态生成障碍物地图的实验也取得了成功。由自主无人机生成的障碍物地图在许多应用中都很有用,例如检查田地,绘制周围环境和救援任务操作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing obstacle's map of an unknown place using autonomous drone navigation and web services
Purpose Drones are used in several purposes including examining areas, mapping surroundings and rescue mission operations. During these tasks, they could encounter compound surroundings having multiple obstacles, acute edges and deadlocks. The purpose of this paper is to propose an obstacle dodging technique required to move the drones autonomously and generate the obstacle's map of an unknown place dynamically. Design/methodology/approach Therefore, an obstacle dodging technique is essentially required to move autonomously. The automaton of drones requires complicated vision sensors and a high computing force. During this research, a methodology that uses two basic ultrasonic-oriented proximity sensors placed at the center of the drone and applies neural control using synaptic plasticity for dynamic obstacle avoidance is proposed. The two-neuron intermittent system has been established by neural control. The synaptic plasticity is used to find turning angles from different viewpoints with immediate remembrance, so it helps in decision-making for a drone. Hence, the automaton will be able to travel around and modify its angle of turning for escaping objects during the route in unknown surroundings with narrow junctions and dead ends. Furthermore, wherever an obstacle is detected during the route, the coordinate information is communicated using RESTful Web service to an android app and an obstacle map is generated according to the information sent by the drone. In this research, the drone is successfully designed and automated and an obstacle map using the V-REP simulation environment is generated. Findings Simulation results show that the drone effectively moves and turns around the obstacles and the experiment of using web services with the drone is also successful in generating the obstacle's map dynamically. Originality/value The obstacle map generated by autonomous drone is useful in many applications such as examining fields, mapping surroundings and rescue mission operations.
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