We consider a mission framework in which two unmanned vehicles (UVs), a leader and a wingmate, are required to provide cooperative coverage of an environment while being within a short communication range. This framework finds applications in underwater and/or military domains, where certain constraints are imposed on communication by either the application or the environment. An important objective of missions within this framework is to minimize the total travel and communication costs of the leader-wingmate duo. In this paper, we propose and formulate the problem of finding routes for the UVs that minimize the sum of their travel and communication costs as a network optimization problem of the form of a binary program (BP). The BP is computationally expensive, with the time required to compute optimal solutions increasing rapidly with the problem size. To address this challenge, here, we propose two algorithms, an approximation algorithm and a heuristic algorithm, to solve large-scale instances of the problem swiftly. We demonstrate the effectiveness and the scalability of these algorithms through an analysis of extensive numerical simulations performed over 500 instances, with the number of targets in the instances ranging from 6 to 100.
翻译:我们考虑一个任务框架,其中要求两辆无人驾驶飞行器(UVs),即领头车和机翼车,在较短的通信范围内合作覆盖环境,这一框架在水下和(或)军事领域找到应用,对通信施加某些限制,无论是应用还是环境。在这个框架内,任务的一个重要目标是尽量减少领导-双翼车的总旅行和通信费用。在本文件中,我们提出并拟订为UV寻找路线的问题,以尽量减少其旅行和通信费用之和,作为二进制程序(BP)形式的网络优化问题。BP是计算成本高昂的,而计算最佳解决办法所需的时间随着问题规模的扩大而迅速增加。为了应对这一挑战,我们在此提出两种算法、一种近似算法和一种超速算法,以迅速解决大规模问题。我们通过分析500多个实例进行的广泛数字模拟,表明这些算法的有效性和可扩缩性,目标数目从6个到100个。