Unmanned aerial vehicle-aided communications system for disaster recovery

The coordination between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is a proactive research topic whose great value of application has attracted vast attention. This paper outlines the motivations for studying the cooperative control of UAVs and UGVs, and attempts to make a comprehensive investigation and analysis on recent research in this field. First, a taxonomy for classification of existing unmanned aerial and ground vehicles systems (UAGVSs) is proposed, and a generalized optimization framework is developed to allow the decision-making problems for different types of UAGVSs to be described in a unified way. By following the proposed taxonomy, we show how different types of UAGVSs can be built to realize the goal of a common task, that is target tracking, and how optimization problems can be formulated for a UAGVS to perform specific tasks. This paper presents an optimization perspective to model and analyze different types of UAGVSs, and serves as a guidance and reference for developing UAGVSs.

Coordination Between Unmanned Aerial and Ground Vehicles: A Taxonomy and Optimization Perspective

Multiple unmanned air/ground vehicles heterogeneous cooperation is a novel and challenging filed. Heterogeneous cooperative techniques can widen the application fields of unmanned air or ground vehicles, and enhance the effectiveness of implementing detection, search and rescue tasks. This paper mainly focused on the key issues in multiple unmanned air/ground vehicles heterogeneous cooperation, including heterogeneous flocking, formation control, formation stability, network control, and actual applications. The main problems and future directions in this field were also analyzed in detail. These innovative technologies can significantly enhance the effectiveness of implementing complicated tasks, which definitely provide a series of novel breakthroughs for the intelligence, integration and advancement of future robot systems.

Unmanned air/ground vehicles heterogeneous cooperative techniques: Current status and prospects

In this paper, we focus on a downlink cellular network, where multiple UAVs serve as aerial basestations to provide wireless connectivity to ground users through frequency division multi-access (FDMA) scheme. The UAVs are exclusively powered by a wireless charging station located on the ground following save-then-transmit protocol. In such a UAV-assisted cellular network, joint optimization for user association, resource allocation and basesation placement are investigated to maximize the downlink sum rate. The problem is formulated as a mixed integer optimization problem and is thus challenging to solve. We propose an efficient solution based on alternating optimization, by iteratively solving one of the three subproblems (i.e., user association, resource allocation and basesation placement) with the other two fixed. Specificly, user association is solved as a standard linear programming problem by relaxing the binary association indicators into continuous variables. For basestation placement and resource allocation, we resort to successive convex optimization technique, which iteratively solves a lower-bound problem. After iteratively solving the three subproblems, we further propose an algorithm based on penalty method and successive convex optimization to make the association indicators feasibly binary. We conduct comprehensive experiments for the optimal solution to the three subproblems with insightful results. We also show that the optimal downlink sum rate cannot be always enhanced by deploying more UAVs due to non-negligible tradeoff between energy/communication sources and co-channel interference. Moreover, the proposed solution outperforms a baseline strategy leveraged from an existing work, especially with favorable channel condition and sufficient frequency resources.

Resource Allocation and Basestation Placement in Downlink Cellular Networks Assisted by Multiple Wireless Powered UAVs

This paper proposes a dynamic mobile ad hoc network (MANET) management system to improve network connectivity by using controlled network nodes, called agents. Agents have predefined wireless communication capabilities similar to the other nodes in the MANET, however their movements, and thus their locations, are dynamically determined to optimize network connectivity. A new approach to measuring connectivity using a maximum flow formulation is proposed - this is both responsive and tractable. Furthermore, users' locations are predicted for several time steps ahead and this is shown to improve network connectivity over the network operation period. A particle swarm optimization (PSO) algorithm uses the maximum flow objective to choose optimal locations of the agents during each time step of network operation. The proposed MANET management system is rigorously tested on numerous static and dynamic problems. Computational results show that the proposed approach is effective in improving the connectivity of MANETs and predicting movements of user nodes and deploying agents accordingly significantly improves the overall performance of a MANET.

Connectivity management in mobile ad hoc networks using particle swarm optimization

In this paper we address the problem of providing full connectivity to disconnected ground MANET nodes by dynamically placing unmanned aerial vehicles (UAVs) to act as relay nodes. We provide a heuristic algorithm to find the minimal number of such aerial vehicles required to provide fall connectivity and find the corresponding locations for these aerial platforms (UAVs). We also track the movement of the ground nodes and update the location of the UAVs. We describe a communication framework that enables the ground nodes to communicate with its peer ground nodes as well as the UAVs that act as relay nodes. The communication architecture is designed to work with existing MANET routing protocols.

M.R. Dekhordi

Papers

Providing full connectivity in large ad-hoc networks by dynamic placement of aerial platforms