Multi-Goal Feasible Path Planning Using Ant Colony Optimization

A new algorithm for solving multi-goal planning problems in the presence of obstacles is introduced. We extend ant colony optimization (ACO) from its well-known application, the traveling salesman problem (TSP), to that of multi-goal feasible path planning for inspection and surveillance applications. Specifically, the ant colony framework is combined with a sampling-based point-to-point planning algorithm; this is compared with two successful sampling-based multi-goal planning algorithms in an obstacle-filled two-dimensional environment. Total mission time, a function of computational cost and the duration of the planned mission, is used as a basis for comparison. In our application of interest, autonomous underwater inspections, the ACO algorithm is found to be the best-equipped for planning in minimum mission time, offering an interior point in the tradeoff between computational complexity and optimality.United States. Office of Naval Research (Grant N00014-06-10043

Dynamical area coverage by mobile sensor networks. Analysis, Modeling and Control

The thesis is a theoretical study of the problem of collecting data from a given field of interest with a team of mobile sensors communicating over an ad-hoc network. It is related with problems of optimal control, ad-hoc networking, distributed computation, computational geometry

Dynamic systems and subadditive functionals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 125-131).Consider a problem where a number of dynamic systems are required to travel between points in minimum time. The study of this problem is traditionally divided into two parts: A combinatorial part that assigns points to every dynamic system and assigns the order of the traversal of the points, and a path planning part that produces the appropriate control for the dynamic systems to allow them to travel between the points. The first part of the problem is usually studied without consideration for the dynamic constraints of the systems, and this is usually compensated for in the second part. Ignoring the dynamics of the system in the combinatorial part of the problem can significantly compromise performance. In this work, we introduce a framework that allows us to tackle both of these parts at the same time. To that order, we introduce a class of functionals we call the Quasi-Euclidean functionals, and use them to study such problems for dynamic systems. We determine the asymptotic behavior of the costs of these problems, when the points are randomly distributed and their number tends to infinity. We show the applicability of our framework by producing results for the Traveling Salesperson Problem (TSP) and Minimum Bipartite Matching Problem (MBMP) for dynamic systems.by Sleiman M. Itani.Ph.D

Dynamic Vehicle Routing for Robotic Systems

Recent years have witnessed great advancements in the science and technology of autonomy, robotics, and networking. This paper surveys recent concepts and algorithms for dynamic vehicle routing (DVR), that is, for the automatic planning of optimal multivehicle routes to perform tasks that are generated over time by an exogenous process. We consider a rich variety of scenarios relevant for robotic applications. We begin by reviewing the basic DVR problem: demands for service arrive at random locations at random times and a vehicle travels to provide on-site service while minimizing the expected wait time of the demands. Next, we treat different multivehicle scenarios based on different models for demands (e.g., demands with different priority levels and impatient demands), vehicles (e.g., motion constraints, communication, and sensing capabilities), and tasks. The performance criterion used in these scenarios is either the expected wait time of the demands or the fraction of demands serviced successfully. In each specific DVR scenario, we adopt a rigorous technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. First, we establish fundamental limits on the achievable performance, including limits on stability and quality of service. Second, we design algorithms, and provide provable guarantees on their performance with respect to the fundamental limits.United States. Air Force Office of Scientific Research (Award FA 8650-07-2-3744)United States. Army Research Office. Multidisciplinary University Research Initiative (Award W911NF-05-1-0219)National Science Foundation (U.S.) (Award ECCS-0705451)National Science Foundation (U.S.) (Award CMMI-0705453)United States. Army Research Office (Award W911NF-11-1-0092