CONTROL 2014: UKACC INTERNATIONAL CONFERENCE ON CONTROL (CONTROL 2014)

# PROGRAM FOR FRIDAY, JULY 11TH

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08:30-10:30 Session 10A: Autonomous Systems
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 08:30 Development of a Network Enabled System for Generic Autonomous VehiclesSPEAKER: Matthew CoombesABSTRACT. This paper describes the development of a system for autonomous vehicle testing, utilising conventional network infrastructure for communication and control; allowing simultaneous control of multiple vehicles of differing vehicle types. A basic level of autonomy is achieved through the use of an Arduino based commercial autopilot (ArduPilot), which also allows for remote vehicle control via MAVLink protocol commands given through serial communication. Traditionally messages are sent using point-to-point wireless serial modems. As these are restricted in terms of bandwidth and flexibility, an improved set-up is suggested, where an embedded computer system is attached to each vehicle. A custom written Node.js program (MAVNode) is then used to encode and decode MAVLink messages onboard allowing communication over a Local Area Network via Wi-Fi. A selection of hardware configurations are discussed, including the use of conventional Wi-Fi and long range Ubiquiti airMAX wireless routers. Both software and hardware in the loop testing is discussed, in addition to the ability to perform control from Matlab/Simulink. With all the infrastructure in place, algorithms can be rapidly prototyped. As an example use of the system, a quad-rotor visually tracks a robot while using a remote Matlab installation for image processing and control. 08:50 Cooperative Source Seeking via Gradient Estimation and Formation Control (Part 1)SPEAKER: Esteban RoseroABSTRACT. In this paper and its companion paper [16], the problem of cooperative source seeking by a formation of mobile agents is considered. Each agent is equipped with position and signal strength measurement sensors; their task is to find the maximum of the scalar field. Agents exchange information with neighboring agents through a communication network. In the first part of this couple of papers, a distributed gradient estimation for each agent and a decentralized navigation controller for single- and double-integrator models are presented. When the signal measurements are corrupted by noise, distributed consensus filters are used in order to estimate the gradient direction. The strategy is based on both a gradient estimating algorithm and a formation controller. Stability conditions are provided. Numerical simulations illustrate the effectiveness of the proposed control law. Part two extends this approach to general linear time-invariant models. 09:10 Cooperative Source Seeking via Gradient Estimation and Formation Control (Part 2)SPEAKER: Esteban RoseroABSTRACT. In this paper and its companion paper [16], the problem of cooperative source seeking by a formation of mobile agents is considered. Each agent is equipped with position and signal strength measurement sensors; their task is to find the maximum of the scalar field. Agents exchange information with neighboring agents through a communication network. In the first part, a distributed gradient estimation for each agent and a decentralized navigation controller for single- and double-integrator models are presented. In this paper, the approach is extended to general linear time-invariant (LTI) models. Stability conditions are provided and our approach is verified using formation flight simulation for quad-rotor helicopters. 09:30 Cooperative Conflict Resolution by Velocity Obstacle MethodSPEAKER: Fatemeh AsadiABSTRACT. The automated and more efficient methods for resolution of conflicts between aircraft is necessary to support the sustained growth of air traffic. Distributed optimization is one of the proposed conflict resolution methods which can improve efficiency; but, sometimes it imposes the unequal burden on involved agents. This paper presents a method for conflict resolution by cooperation between agents which can lead to fair contribution of all agents in resolving the collision. 09:50 Estimation Of Time To Point Of Closest Approach For Collision Avoidance And Separation SystemsSPEAKER: James DunthorneABSTRACT. This paper proposes a method for estimating the time until two aircraft are at the their point of closest approach (TPCA). A range of simple methods, which use derivatives to estimate the time to collision, are analysed. These methods are only accurate when the angle subtended between the direction of the relative velocity vector and the bearing of the intruder aircraft, $\theta$, is small. An extended method is developed which calculates the exact TPCA from distance and bearing measurements. Representative levels of Gaussian white noise are introduced to the core equation variables for both the derivative and extended methods. It is found that as we increase the value of $\theta$, the extended method's accuracy increases beyond that of the derivative method. A fusion algorithm is developed to switch between methods and is shown to perform well for a range of conflicts. When the relative velocity between the two aircraft is small, the signal to noise ratio on the relative velocity variable reduces causing large errors to the TPCA estimation. It is therefore concluded that at a certain relative velocity threshold, $k_V$ (dependant on sensor and filter performance) both the derivative and extended TPCA estimation methods would become undesirable as risk estimators. It is suggested that in these situations distance could be better to use since it can be measured directly. 10:10 Ant Colony Optimization for Routing and Tasking Problems for Teams of UAVsSPEAKER: Theopisti ZazaABSTRACT. This paper presents an enhanced version of the ant colony optimization (ACO) for solving an improved model of vehicles routing problem (VRP), which is utilized for Unmanned Aerial Vehicle (UAV) task loitering and route planning. The improved VRP incorporates collision avoidance penalties, not only for the intersections between the vehicle's routes, but also for their departure and landing times. The ant colony algorithm uses a single objective function, consisting of penalties, ensuring in that way that the solutions with intersections will be evaluated. The ACO is a variation of the already known multi-colony algorithm where several ant colonies are assigned to different loitering steps for the same route between two tasks. Numerical experiments and comparison to previous work are illustrated to demonstrate the efficiency of the proposed algorithm.
08:30-10:30 Session 10B: Model Predictive Control
Chairs: