Multi-agent routing in shared guidepath networks

Motivated by a broad spectrum of applications ranging from automated zone-controlled, unit-load material handling systems to the movement of ions within a quantum computer, this thesis considers a class of multi-agent routing problems that seek to minimize the agents’ traveling time subject to certain congestion constraints. In more technical terms, the particular problem addressed in this work concerns the development of efficient, conflict-free, and deadlock-free schedules to route a set of non-interchangeable “agents” between their respective starting locations and destinations. Routes are specified as sequences of adjacent edges of the guidepath network, that are allocated sequentially and exclusively to the traveling agents by a traffic coordinator, according to an allocation protocol that seeks to ensure physical feasibility and other notions of “safety” for the agent motion. On the other hand, efficiency is measured by the schedule “makespan”—i.e., the time required for all agents to reach their respective destinations. In order to formally characterize the addressed scheduling problem and the corresponding notion of optimality for the sought schedules, this thesis first formulates the problem as a mixed-integer program (MIP). In this formulation, the system state at a given time is defined by the allocated edges and the directions of travel for the various agents, and the system is assumed to evolve this state at discrete time intervals that are defined by the required edge-traversal times. The presented MIP is derived according to a resource allocation system (RAS) perspective, and it is based on a set of binary decision variables that characterize the evolution of the system state over a sufficiently long time horizon. An additional auxiliary variable allows the computation of the schedule "makespan"—i.e., the number of discrete time periods required for the last agent to reach its designated destination.  An important feature of the developed MIP formulation is its ability to accommodate a broad range of variations of the considered traffic-scheduling problem that result from the variation of certain structural elements of the underlying traffic system and of the adopted edge-allocation protocol. From a computational standpoint, the optimal solution of all these problems is very complex. In many cases, even the identification of a feasible solution for a given problem instance can be a challenging problem. In view of all this complexity, the second part of the thesis formulates a Lagrangian dual problem for the generation of lower bounds for the original scheduling problem, and then describes two distinct methods to optimize this dual problem: (i) a customized dual-ascent algorithm, and (ii) a reformulation of the dual problem as a single, large linear program (LP). The first approach is proven to find an exact solution in a finite number of iterations, but the availability of very efficient LP solvers renders the second method more robust for larger problem instances. The two approaches provide consistent lower bounds for the optimal makespans of various problem instances, as well as Lagrange multipliers that optimize the Lagrangian dual and may be useful in the guidance of other heuristic algorithms for an optimized schedule. The third part of the thesis presents and analyzes a heuristic, "local-search" type of algorithm for minimizing the makespans of multi-agent routes on a shared guidepath network. For the context of conflict-free ion routing within a quantum computer, the thesis describes a complete algorithm for finding an initial feasible solution, and for optimizing that schedule by iterative reduction of the makespan, using dynamic programming (DP) to revise agent routes while eliminating conflicts between agents. Various methods for strengthening the makespan-reduction procedure (e.g., multi-agent simultaneous route revision, or controlled excursions into the infeasible region) are described and analyzed. Finally, the dissertation provides a set of experimental results that are obtained from the implementation of the developed methods for a carefully selected set of problem instances. For each instance, we find lower bounds (obtained either by hand, or by solving the Lagrangian dual problem) on the optimal objective values, as well as actual makespans for feasible schedules discovered by the heuristic scheduler. The considered problem instances include: (i) a small but difficult problem used to motivate our early research; (ii) a more complex "challenge" problem designed to maximize congestion; and (iii) a series of 150 randomized trials formulated on a grid-based configuration of the guidepath network that is typical of the corresponding structures that are encountered in many practical applications. The third set of experiments is further designed to evaluate the performance of the heuristic scheduler under increasing levels of congestion. The obtained results reveal that our heuristic algorithm can provide very efficient solutions for the targeted variations of the guidepath-based traffic-scheduling problem, in a way that is computationally efficient and complete. The thesis concludes with suggestions for future research that are aimed at (a) the further enhancement of the heuristic algorithm, (b) the extension of this algorithm and of the corresponding methodology to other variations of the considered traffic-scheduling problems, and (c) the embedding of all these results in a broader “rolling-horizon” framework that will address the dynamic nature of the operational (i.e., the transport) requirements of the considered traffic systems.Ph.D

Route planning of automated guided vehicles for container logistics

Automated guided vehicles (AGVs) are widely used in container terminals for the movement of material from shipping to the yard area and vice versa. Research in this area is directed toward the development of a path layout design and routing algorithms for container movement. The problem is to design a path layout and a routing algorithm that will route the AGVs along the bi-directional path so that the distance traveled will be minimized. This thesis presents a bi-directional path flow layout and a routing algorithm that guarantee conflict-free, shortest time routes for AGVs. Based on the path layout, a routing algorithm and sufficient, but necessary conditions, mathematical relationships are developed among certain key parameters of vehicle and path. A high degree of concurrency is achieved in the vehicle movement. The routing efficiency is analyzed in terms of the distance traveled and the time required for AGVs to complete all pickup and drop-off jobs. Numerical results are presented to compare performance of the proposed model. The research provides the foundation for a bi-directional path layout design and routing algorithms that will aid the designer to develop complicated path layouts

Behavior Classification, Security, and Consensus in Societies of Robots

This thesis addresses some fundamental issues toward the realization of "societies" of robots. This objective requires dealing with large numbers of heterogenous autonomous systems, differing in their bodies, sensing and intelligence, that are made to coexist, communicate, learn and classify, and compete fairly, while achieving their individual goals. First, as in human or animal societies, robots must be able to perform cooperative "behaviors" that involve coordination of their actions, based on their own goals, proprioceptive sensing, and information they can receive from other neighboring robots. An effective way to successfully achieve cooperation is obtained by requiring that robots share a set of decentralized motion "rules" involving only locally available data. A first contribution of the thesis consists in showing how these behaviors can be nicely described by a suitable hybrid formalism, including the heterogenous dynamics of every robots and the above mentioned rules that are based on events. A second contribution deals with the problem of classifying a set of robotic agents, based on their dynamics or the interaction protocols they obeys, as belonging to different "species". Various procedures are proposed allowing the construction of a distributed classification system, based on a decentralized identification mechanism, by which every agent classifies its neighbors using only locally available information. By using this mechanism, members of the society can reach a consensus on the environment and on the integrity of the other neighboring robots, so as to improve the overall security of the society. This objective involves the study of convergence of information that is not represented by real numbers, as often in the literature, rather by sets. The dynamics of the evolution of information across a number of robots is described by set-valued iterative maps. While the study of convergence of set-valued iterative maps is highly complex in general, this thesis focuses on Boolean maps, which are comprised of arbitrary combinations of unions, intersections, and complements of sets. Through the development of an industrial robotic society, it is finally shown how the proposed technique applies to a real and commercially relevant case-study. This society sets the basis for a full-fledged factory of the future, where the different and heterogeneous agents operate and interact using a blend of autonomous skills, social rules, and central coordination

Task Allocation and Collaborative Localisation in Multi-Robot Systems

To utilise multiple robots, it is fundamental to know what they should do, called task allocation, and to know where the robots are, called localisation. The order that tasks are completed in is often important, and makes task allocation difficult to solve (40 tasks have 1047 different ways of completing them). Algorithms in literature range from fast methods that provide reasonable allocations, to slower methods that can provide optimal allocations. These algorithms work well for systems with identical robots, but do not utilise robot differences for superior allocations when robots are non-identical. They also can not be applied to robots that can use different tools, where they must consider which tools to use for each task. Robot localisation is performed using sensors which are often assumed to always be available. This is not the case in GPS-denied environments such as tunnels, or on long-range missions where replacement sensors are not readily available. A promising method to overcome this is collaborative localisation, where robots observe one another to improve their location estimates. There has been little research on what robot properties make collaborative localisation most effective, or how to tune systems to make it as accurate as possible. Most task allocation algorithms do not consider localisation as part of the allocation process. If task allocation algorithms limited inter-robot distance, collaborative localisation can be performed during task completion. Such an algorithm could equally be used to ensure robots are within communication distance, and to quickly detect when a robot fails. While some algorithms for this exist in literature, they provide a weak guarantee of inter-robot distance, which is undesirable when applied to real robots. The aim of this thesis is to improve upon task allocation algorithms by increasing task allocation speed and efficiency, and supporting robot tool changes. Collaborative localisation parameters are analysed, and a task allocation algorithm that enables collaborative localisation on real robots is developed. This thesis includes a compendium of journal articles written by the author. The four articles forming the main body of the thesis discuss the multi-robot task allocation and localisation research during the author’s candidature. Two appendices are included, representing conference articles written by the author that directly relate to the thesis.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS 1994), volume 1

The AIAA/NASA Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS '94) was originally proposed because of the strong belief that America's problems of global economic competitiveness and job creation and preservation can partly be solved by the use of intelligent robotics, which are also required for human space exploration missions. Individual sessions addressed nuclear industry, agile manufacturing, security/building monitoring, on-orbit applications, vision and sensing technologies, situated control and low-level control, robotic systems architecture, environmental restoration and waste management, robotic remanufacturing, and healthcare applications

Technology 2004, Vol. 2

Proceedings from symposia of the Technology 2004 Conference, November 8-10, 1994, Washington, DC. Volume 2 features papers on computers and software, virtual reality simulation, environmental technology, video and imaging, medical technology and life sciences, robotics and artificial intelligence, and electronics

IAIMS newsletter

NewsletterThe IAIMS Newsletter (1996-2005) provides valuable information about library activities and resources as well as informative articles related to information technology