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

This paper presents a survey of recent research in cooperative control of multivehicle systems, using a common mathematical framework to allow different methods to be described in a unified way. The survey has three primary parts: an overview of current applications of cooperative control, a summary of some of the key technical approaches that have been explored, and a description of some possible future directions for research. Specific technical areas that are discussed include formation control, cooperative tasking, spatiotemporal planning, and consensus.

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Recent Research in Cooperative Control of Multivehicle Systems

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

General techniques for constructing vector fields for unmanned aircraft guidance are provided that incorporate Lyapunov stability properties to produce simple, globally stable vector fields in three dimensions. Use of these fields to produce circular loiter pattern attractors is illustrated, along with a simple switching algorithm to enable following of arbitrary way point sequences. Alternatively, attractor shape variations are developed by warping the circular loiter, preserving global stability guarantees, and accurate path tracking. An example of this technique is provided that produces a racetrack loiter pattern, and three different variations in the warping technique are compared. Finally, tracking of the vector field is considered, using Lyapunov techniques to show global stability of heading and path position for several types of tracking control laws that are compatible with low cost unmanned aircraft avionics.

Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control

Simulated annealing for resource-constrained scheduling

This paper considers cooperative path planning for aerial munitions during the attack phase of a mission against ground targets. It is assumed that sensor information from multiple munitions is available to refine an estimate of the target location. Based on models of the munition dynamics and sensor performance, munition trajectories are designed that enhance the ability to cooperatively estimate the target location. The problem is posed as an optimal control problem using a cost function based on the variances in the target-location estimate. These variances are computed by fusing the individual munition measurements in a weighted least-squares estimate. Solutions to the problem are found using a direct-shooting method. These solutions are compared with trajectories developed by an alternative suboptimal feedback-guidance law. This feedback law produces solutions with far less numerical expense and with a performance very close to the best known solutions. The reduction in target-location uncertainty associated with these trajectories could enable the attack of targets with greater precision using smaller, cheaper munitions.

Optimal and Feedback Path Planning for Cooperative Attack

Analysis of dynamic sensor coverage problem using Kalman filters for estimation

This paper presents the Target Visitation Problem (TVP) for a single unmanned aerial vehicle (UAV). The ability to effectively plan a path for a UAV to visit multiple targets is an increasingly important capability in a variety of applications, including surveillance, attack, assessment, search and rescue, disaster relief, and environmental cleanup. The TVP is related to both the Traveling Salesman Problem and the Linear Ordering Problem, with an objective function that combines elements of both problems. The TVP considers both total travel distance and the order of targets visited. In this paper, we formulate the target visitation problem for a single vehicle, describe a heuristic solution procedure, and report results for problems of various size.

Formulation and Solution of the Target Visitation Problem

We study the problem of using a small number of mobile sensors to monitor various threats in a geographical area. Using some recent results on stochastic sensor scheduling, we propose a stochastic sensor movement strategy. We present simple conditions under which it is not possible to maintain a bounded estimate error covariance for all the threats. We also study a simple sub-optimal algorithm to generate stochastic trajectories. Simulations are presented to illustrate the results.

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David E. Jeffcoat

Papers

Simulated annealing for resource-constrained scheduling

Optimal and Feedback Path Planning for Cooperative Attack

Analysis of dynamic sensor coverage problem using Kalman filters for estimation

Formulation and Solution of the Target Visitation Problem

On Sensor Coverage by Mobile Sensors