This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Solar Engineering Of Thermal Processes

The Traveling Salesman Problem A Computational Study

UAV assistance paradigm: State-of-the-art in applications and challenges

This paper implements robust decentralized model predictive control (DMPC) for a team of cooperating uninhabited aerial vehicles (UAVs). The problem involves vehicles with independent dynamics but with coupled constraints to capture required cooperative behavior. Using a recently-developed form of DMPC, each vehicle plans only for its own actions, but feasibility of the sub-problems and satisfaction of the coupling constraints are guaranteed throughout, despite the action of unknown but bounded disturbances. UAVs communicate relevant plan data to ensure that decisions are consistent across the team. Collision avoidance is used as an example of coupled constraints and the paper shows how the speed, turn rate and avoidance distance limits in the optimization should be modified in order to guarantee robust constraint satisfaction. Integer programming is used to solve the non-convex problem of path-planning subject to avoidance constraints. Numerical simulations compare computation time and target arrival time under decentralized and centralized control and investigate the impact of decentralization on team performance. The results show that the computation required for DMPC is significantly lower than for its centralized counterpart and scales better with the size of the team, at the expense of only a small increase in UAV flight times.

Decentralized model predictive control of co-operating UAVs

This letter studies the network performance improvement by deploying flying base stations mounted on unmanned aerial vehicles (UAV-BSs) during some occasional events. Specifically, we consider the problem to minimize the average UAV-user distance while keeping the UAV-BSs connected to the stationary base stations. We propose a distributed algorithm which requires only local information and is scalable to a large number of UAV-BSs. We prove its convergence and conduct simulations with a real data set to evaluate its performance.

Deployment of Unmanned Aerial Vehicle Base Stations for Optimal Quality of Coverage

Mobile Robots in Wireless Sensor Networks: A Survey on Tasks

This paper focuses on the application of surveillance and monitoring using unmanned aerial vehicles (UAVs). A novel coverage model is proposed to characterize the quality of coverage (QoC) of a target by a UAV. On the basis of this model, a reactive collision free three-dimensional deployment algorithm is proposed, with the goal of maximizing the overall QoC of targets by a network of UAVs. The algorithm consists of two navigation laws for the horizontal movement and the vertical movement, both of which are easily implementable in real time. The convergence of the algorithm is proved, and the computational complexity is analyzed. Computer simulations are conducted to demonstrate the performance of the proposed method.

An Algorithm of Reactive Collision Free 3-D Deployment of Networked Unmanned Aerial Vehicles for Surveillance and Monitoring

We focus on the problem of deploying unmanned aerial vehicles to service mobile users in a cellular network, with the aim of maximizing coverage and reducing interference effects. An optimization model that is distributed in nature is proposed and a maximizing algorithm is developed to find a locally optimal solution. The performance of this distributed algorithm is shown to be superior in quality and solution time to a standard greedy algorithm. Testing on a simulation of a practical scenario is performed to demonstrate the application of the method to real scenarios as well as to illustrate the tradeoff between maximizing coverage and minimizing interference.

A Method for Optimized Deployment of Unmanned Aerial Vehicles for Maximum Coverage and Minimum Interference in Cellular Networks

This letter considers the deployment problem for a network of aerial drones to maximize the coverage of an area for surveillance and monitoring. A distributed optimization model is proposed and a coverage maximizing algorithm is developed. It is proved that the proposed algorithm converges to a local maximum in a finite number of steps. Simulations with a real dataset demonstrate the effectiveness of the algorithm.

Hailong Huang

Papers

Deployment of Unmanned Aerial Vehicle Base Stations for Optimal Quality of Coverage

Mobile Robots in Wireless Sensor Networks: A Survey on Tasks

An Algorithm of Reactive Collision Free 3-D Deployment of Networked Unmanned Aerial Vehicles for Surveillance and Monitoring

A Method for Optimized Deployment of Unmanned Aerial Vehicles for Maximum Coverage and Minimum Interference in Cellular Networks

A Method for Optimized Deployment of a Network of Surveillance Aerial Drones