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 reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

/pdf/an-overview-of-recent-progress-in-the-study-of-distributed-1xnb6e12se.pdf

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This is the fifth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods $the use of multiple models (and filters) simultaneously - which is the prevailing approach to maneuvering target tracking in recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.

Survey of maneuvering target tracking. Part V. Multiple-model methods

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.

/pdf/recent-research-in-cooperative-control-of-multivehicle-4aw1z97kwj.pdf

Recent Research in Cooperative Control of Multivehicle Systems

Effective Task Assignment for Complex Uav Operations Using Genetic Algorithms

On guaranteeing point capture in linear n-on-1 endgame interception engagements with bounded controls

Intercept Angle Guidance in an Obstacle-Rich Environment

Sliding mode guidance laws for intercepting high speed targets in a novel head-pursuit engagement are presented. The guidance laws impose a geometric relation that positions the interceptor missile ahead of the target, on its ∞ight trajectory, so that both ∞y in the same direction. The missile speed is planned to be lower than that of the target, and therefore the target closes in on the interceptor missile. Using this approach the closing speed is signiflcantly reduced relative to a head-on engagement; and compared to a tail-chase engagement, the low closing speed is achieved with reduced energy requirements. The guidance laws are derived for an aerodynamically controlled endo-atmospheric interceptor as well as for an exo-atmospheric kill vehicle controlled by divert thrusters. Simulation results conflrm the viability of the proposed sliding mode guidance laws in several representative engagements against maneuvering high-speed targets.

Precursor Interceptor Guidance Using the Sliding Mode Approach

The motion planning problem of a single autonomous vehicle having a minimum turn radius constraint, visiting an ordered sequence of targets in an environment with polygonal obstacles, is addressed. Two types of vehicles are considered: aerial/ground vehicle - described by the Dubins/Reeds-Shepp vehicle models, respectively. The problem is posed in the form of a search tree by using the obstacles’ vertices, vehicle’s initial configuration, and the set of target points as nodes. The tree’s arcs are represented by the Dubins/Reed-Shepp paths without terminal angle constraint (relaxed paths) connecting two adjacent nodes. These relaxed paths - connecting an initial configuration and a destination, are calculated using a feedback algorithm. Due to the computational complexity of the problem a genetic algorithm is proposed. Additionally, two deterministic search algorithms are presented. A quick heuristic greedy algorithm which uses the visibility graph distances for estimating the remaining vehicle path and an exhaustive algorithm which provides optimal solution trajectories. The performance of the algorithms is demonstrated and compared through sample runs and a Monte Carlo study. Results confirm that the heuristic algorithm provides relatively good solution for a small radius turn vehicle, while the genetic algorithm offers a good trade-off between computational load and performance.

Tal Shima

Papers

Effective Task Assignment for Complex Uav Operations Using Genetic Algorithms

On guaranteeing point capture in linear n-on-1 endgame interception engagements with bounded controls

Intercept Angle Guidance in an Obstacle-Rich Environment

Precursor Interceptor Guidance Using the Sliding Mode Approach

Multi-Target Motion Planning Amidst Obstacles for Autonomous Aerial and Ground Vehicles