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Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
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ItemAutomatic coordination and deployment of multi-robot systems(Georgia Institute of Technology, 2009-03-31) Smith, Brian StephenWe present automatic tools for configuring and deploying multi-robot networks of decentralized, mobile robots. These methods are tailored to the decentralized nature of the multi-robot network and the limited information available to each robot. We present methods for determining if user-defined network tasks are feasible or infeasible for the network, considering the limited range of its sensors. To this end, we define rigid and persistent feasibility and present necessary and sufficient conditions (along with corresponding algorithms) for determining the feasibility of arbitrary, user-defined deployments. Control laws for moving multi-robot networks in acyclic, persistent formations are defined. We also present novel Embedded Graph Grammar Systems (EGGs) for coordinating and deploying the network. These methods exploit graph representations of the network, as well as graph-based rules that dictate how robots coordinate their control. Automatic systems are defined that allow the robots to assemble arbitrary, user-defined formations without any reliance on localization. Further, this system is augmented to deploy these formations at the user-defined, global location in the environment, despite limited localization of the network. The culmination of this research is an intuitive software program with a Graphical User Interface (GUI) and a satellite image map which allows users to enter the desired locations of sensors. The automatic tools presented here automatically configure an actual multi-robot network to deploy and execute user-defined network tasks.
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ItemOptimal, Multi-Modal Control with Applications in Robotics(Georgia Institute of Technology, 2007-04-04) Mehta, Tejas R.The objective of this dissertation is to incorporate the concept of optimality to multi-modal control and apply the theoretical results to obtain successful navigation strategies for autonomous mobile robots. The main idea in multi-modal control is to breakup a complex control task into simpler tasks. In particular, number of control modes are constructed, each with respect to a particular task, and these modes are combined according to some supervisory control logic in order to complete the overall control task. This way of modularizing the control task lends itself particularly well to the control of autonomous mobile robot, as evidenced by the success of behavior-based robotics. Many challenging and interesting research issues arise when employing multi-modal control. This thesis aims to address these issues within an optimal control framework. In particular, the contributions of this dissertation are as follows: We first addressed the problem of inferring global behaviors from a collection of local rules (i.e., feedback control laws). Next, we addressed the issue of adaptively varying the multi-modal control system to further improve performance. Inspired by adaptive multi-modal control, we presented a constructivist framework for the learning from example problem. This framework was applied to the DARPA sponsored Learning Applied to Ground Robots (LAGR) project. Next, we addressed the optimal control of multi-modal systems with infinite dimensional constraints. These constraints are formulated as multi-modal, multi-dimensional (M3D) systems, where the dimensions of the state and control spaces change between modes to account for the constraints, to ease the computational burdens associated with traditional methods. Finally, we used multi-modal control strategies to develop effective navigation strategies for autonomous mobile robots. The theoretical results presented in this thesis are verified by conducting simulated experiments using Matlab and actual experiments using the Magellan Pro robot platform and the LAGR robot. In closing, the main strength of multi-modal control lies in breaking up complex control task into simpler tasks. This divide-and-conquer approach helps modularize the control system. This has the same effect on complex control systems that object-oriented programming has for large-scale computer programs, namely it allows greater simplicity, flexibility, and adaptability.
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ItemGraph-based Path Planning for Mobile Robots(Georgia Institute of Technology, 2006-11-16) Wooden, David T.In this thesis, questions of navigation, planning and control of real-world mobile robotic systems are addressed. Chapter II contains the first contribution in this thesis, which is a modification of the canonical two-layer hybrid architecture: deliberative planning on top, with reactive behaviors underneath. Deliberative is used to describe higher-level reasoning that includes experiential memory and regional or global objectives. Alternatively, reactive describes low-level controllers that operate on information spatially and temporally immediate to the robot. In the traditional architecture, information is passed top down, with the deliberative layer dictating to the reactive layer. Chapter II presents our work on introducing feedback in the opposite direction, allowing the behaviors to provide information to the planning module(s). The path planning problem, particularly as it as solved by the visibility graph, is addressed first in Chapter III. Our so-called oriented visibility graph is a combinatorial planner with emphasis on dynamic re-planning in unknown environments at the expensive of guaranteed optimality at all times. An example of single source planning -- where the goal location is known and static -- this approach is compared to related approaches (e.g. the reduced visibility graph). The fourth chapter further develops the work presented in the Chapter III; the oriented visibility graph is extended to the hierarchical oriented visibility graph. This work directly addresses some of the limitations of the oriented visibility graph, particularly the loss of optimality in the case where obstacles are non-convex and where the convex hulls of obstacles overlap. This results in an approach that is a kind of middle-ground between the oriented visibility graph which was designed to handle dynamic updates very fast, and the reduced visibility graph, an old standard in path planning that guarantees optimality. Chapter V investigates path planning at a higher level of abstraction. Given is a weighted colored graph where vertices are assigned a color (or class) that indicates a feature or quality of the environment associated with that vertex. The question is then asked, ``what is the globally optimal path through this weighted colored graph?' We answer this question with a mapping from classes and edge weights to a real number, and use Dijkstra's Algorithm to compute the best path. Correctness is proven and an implementation is highlighted.
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ItemOptimal Control of Switched Autonomous Systems: Theory, Algorithms, and Robotic Applications(Georgia Institute of Technology, 2006-04-05) Axelsson, HenrikAs control systems are becoming more and more complex, system complexity is rapidly becoming a limiting factor in the efficacy of established techniques for control systems design. To cope with the growing complexity, control architectures often have a hierarchical structure. At the base of the system pyramid lie feedback loops with simple closed-loop control laws. These are followed, at a higher level, by discrete control logics. Such hierarchical systems typically have a hybrid nature. A common approach to addressing these types of complexity consists of decomposing, in the time domain, the control task into a number of modes, i.e. control laws dedicated to carrying out a limited task. This type of control generally involves switching laws among the various modes, and its design poses a major challenge in many application domains. The primary goal of this thesis is to develop a unified framework for addressing this challenge. To this end, the contribution of this thesis is threefold: 1. An algorithmic framework for how to optimize the performance of switched autonomous systems is derived. The optimization concerns both the sequence in which different modes appear in and the duration of each mode. The optimization algorithms are presented together with detailed convergence analyses. 2. Control strategies for how to optimize switched autonomous systems operating in real time, and when the initial state of the system is unknown, are presented. 3. A control strategy for how to optimally navigate an autonomous mobile robot in real-time is presented and evaluated on a mobile robotics platform. The control strategy uses optimal switching surfaces for when to switch between different modes of operations (behaviors).
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ItemGraphs, Simplicial Complexes and Beyond: Topological Tools for Multi-agent Coordination(Georgia Institute of Technology, 2005-12-16) Muhammad, AbubakrIn this work, connectivity graphs have been studied as models of local interactions in multi-agent robotic systems. A systematic study of the space of connectivity graphs has been done from a geometric and topological point of view. Some results on the realization of connectivity graphs in their respective configuration spaces have been given. A complexity analysis of networks, from the point of view of intrinsic structural complexity, has been given. Various topological spaces in networks, as induced from their connectivity graphs, have been recognized and put into applications, such as those concerning coverage problems in sensor networks. A framework for studying dynamic connectivity graphs has been proposed. This framework has been used for several applications that include the generation of low-complexity formations as well as collaborative beamforming in sensor networks. The theory has been verified by generating extensive simulations, with the help of software tools of computational homology and semi-definite programming. Finally, several open problems and areas of further research have been identified.