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44
Rough Terrain Autonomous Mobility  Part 2: An Active . . .
 AUTONOMOUS ROBOTS
, 1998
"... Offroad autonomous navigation is one of the most difficult automation challenges from the point of view of constraints on mobility, speed of motion, lack of environmental structure, density of hazards, and typical lack of prior information. This paper describes an autonomous navigation software sys ..."
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Cited by 36 (11 self)
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Offroad autonomous navigation is one of the most difficult automation challenges from the point of view of constraints on mobility, speed of motion, lack of environmental structure, density of hazards, and typical lack of prior information. This paper describes an autonomous navigation software system for outdoor vehicles which includes perception, mapping, obstacle detection and avoidance, and goal seeking. It has been used on sev eral vehicle testbeds including autonomous HMMWV's and planetary rover prototypes. To date, it has achieved speeds of 15 km/hr and excursions of 15 km. We introduce algorithms for optimal processing and computational stabilization of range imagery for terrain mapping purposes. We formulate the problem of trajectory generation as one of predictive control searching trajectories expressed in command space. We also formulate the problem of goal arbitration in local autonomous mobility as an optimal control problem. We emphasize the modeling of vehicles in ...
Lyapunov Design for Safe Reinforcement Learning
 Journal of Machine Learning Research
"... Lyapunov design methods are used widely in control engineering to design controllers that achieve qualitative objectives, such as stabilizing a system or maintaining a system's state in a desired operating range. We propose a method for constructing safe, reliable reinforcement learning agents ba ..."
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Cited by 16 (2 self)
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Lyapunov design methods are used widely in control engineering to design controllers that achieve qualitative objectives, such as stabilizing a system or maintaining a system's state in a desired operating range. We propose a method for constructing safe, reliable reinforcement learning agents based on Lyapunov design principles. In our approach, an agent learns to control a system by switching among a number of given, baselevel controllers. These controllers are designed using Lyapunov domain knowledge so that any switching policy is safe and enjoys basic performance guarantees. Our approach thus ensures qualitatively satisfactory agent behavior for virtually any reinforcement learning algorithm and at all times, including while the agent is learning and taking exploratory actions. We demonstrate the process of designing safe agents for four dierent control problems. In simulation experiments, we nd that our theoretically motivated designs also enjoy a number of practical benets, including reasonable performance initially and throughout learning, and accelerated learning. Keywords: Reinforcement Learning, Lyapunov Functions, Safety, Stability 1.
From Biological Models to the Evolution of Robot Control Systems
 Philosophical Transactions of the Royal Society of London A
, 2003
"... Attempts to formulate realistic... In this paper I shall describe some of the aspects of these biological models that are likely to be useful for building robot control systems. In particular, I shall consider the evolution of appropriate innate starting points for learning/adaptation, patterns of l ..."
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Cited by 12 (10 self)
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Attempts to formulate realistic... In this paper I shall describe some of the aspects of these biological models that are likely to be useful for building robot control systems. In particular, I shall consider the evolution of appropriate innate starting points for learning/adaptation, patterns of learning rates that vary across different system components, learning rates that vary during the system's lifetime, and the relevance of individual differences across the evolved populations
Solving LinearQuadratic Optimal Control Problems on Parallel Computers
, 2007
"... We discuss a parallel library of efficient algorithms for the solution of linearquadratic optimal control problems involving largescale systems with statespace dimension up to O(10 4). We survey the numerical algorithms underlying the implementation of the chosen optimal control methods. The appr ..."
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Cited by 11 (10 self)
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We discuss a parallel library of efficient algorithms for the solution of linearquadratic optimal control problems involving largescale systems with statespace dimension up to O(10 4). We survey the numerical algorithms underlying the implementation of the chosen optimal control methods. The approaches considered here are based on invariant and deflating subspace techniques, and avoid the explicit solution of the associated algebraic Riccati equations in case of possible illconditioning. Still, our algorithms can also optionally compute the Riccati solution. The major computational task of finding spectral projectors onto the required invariant or deflating subspaces is implemented using iterative schemes for the sign and disk functions. Experimental results report the numerical accuracy and the parallel performance of our approach on a cluster of Intel Itanium2 processors.
Model reduction based on spectral projection methods
 Dimension Reduction of LargeScale Systems
, 2005
"... We discuss the efficient implementation of model reduction methods such as modal truncation, balanced truncation, and other balancingrelated truncation techniques, employing the idea of spectral projection. Mostly, we will be concerned with the sign function method which serves as the major computa ..."
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Cited by 9 (6 self)
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We discuss the efficient implementation of model reduction methods such as modal truncation, balanced truncation, and other balancingrelated truncation techniques, employing the idea of spectral projection. Mostly, we will be concerned with the sign function method which serves as the major computational tool of most of the discussed algorithms for computing reducedorder models. Implementations for largescale problems based on parallelization or formatted arithmetic will also be discussed. This chapter can also serve as a tutorial on Gramianbased model reduction using spectral projection methods. 1
Robustness under Bounded Uncertainty with Phase Information
, 1998
"... We consider uncertain linear systems where the uncertainties, in addition to being bounded, also satisfy constraints on their phase. In this context, we define the "phasesensitive structured singular value" (PSSSV) of a matrix, and show that sufficient (and sometimes necessary) conditions for stab ..."
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Cited by 6 (2 self)
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We consider uncertain linear systems where the uncertainties, in addition to being bounded, also satisfy constraints on their phase. In this context, we define the "phasesensitive structured singular value" (PSSSV) of a matrix, and show that sufficient (and sometimes necessary) conditions for stability of such uncertain linear systems can be rewritten as conditions involving PSSSV. We then derive upper bounds for PSSSV, computable via convex optimization. We extend these results to the case where the uncertainties are structured (diagonal or blockdiagonal, for instance). 1 Introduction A popular paradigm for modeling control systems with uncertainties is illustrated in Fig. 1. Here P (s) is the transfer function of a stable linear system, and \Delta is a stable operator that represents the "uncertainties" that arise from various sources such as modeling errors, neglected or unmodeled dynamics or parameters, etc. Such control system models have found wide acceptance in robust con...
A framework for online adaptive control of problem solving
 In Proc. of CP2001 workshop on OnLine combinatorial problem solving and Constraint Programming, Paphos
, 2001
"... The design of a problem solver for a particular problem depends on the problem type, the system resources, and the application requirements, as well as the specific problem instance. The difficulty in matching a solver to a problem can be ameliorated through the use of online adaptive control of sol ..."
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Cited by 5 (1 self)
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The design of a problem solver for a particular problem depends on the problem type, the system resources, and the application requirements, as well as the specific problem instance. The difficulty in matching a solver to a problem can be ameliorated through the use of online adaptive control of solving. In this approach, the solver or problem representation selection and parameters are defined appropriately to the problem structure, environment models, and dynamic performance information, and the rules or model underlying this decision are adapted dynamically. This paper presents a general framework for the adaptive control of solving and discusses the relationship of this framework both to adaptive techniques in control theory and to the existing adaptive solving literature. Experimental examples are presented to illustrate the possible uses of solver control. 1
Gramianbased model reduction for datasparse systems
, 2007
"... Model reduction is a common theme within the simulation, control and optimization of complex dynamical systems. For instance, in control problems for partial differential equations, the associated largescale systems have to be solved very often. To attack these problems in reasonable time it is abs ..."
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Cited by 4 (4 self)
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Model reduction is a common theme within the simulation, control and optimization of complex dynamical systems. For instance, in control problems for partial differential equations, the associated largescale systems have to be solved very often. To attack these problems in reasonable time it is absolutely necessary to reduce the dimension of the underlying system. We focus on model reduction by balanced truncation where a system theoretical background provides some desirable properties of the reducedorder system. The major computational task in balanced truncation is the solution of largescale Lyapunov equations, thus the method is of limited use for really largescale applications. We develop an effective implementation of balancingrelated model reduction methods in exploiting the structure of the underlying problem. This is done by a datasparse approximation of the largescale state matrix A using the hierarchical matrix format. Furthermore, we integrate
Distributed adaptive constrained optimization for smart matter systems
 In Proceedings of AAAI Spring Symposium on Intelligent Embedded and Distributed Systems
, 2002
"... The remarkable increase in computing power together with a similar increase in sensor and actuator capabilities now under way is enabling a significant change in how systems can sense and manipulate their environment. These changes require control algorithms capable of operating a multitude of inter ..."
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Cited by 4 (0 self)
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The remarkable increase in computing power together with a similar increase in sensor and actuator capabilities now under way is enabling a significant change in how systems can sense and manipulate their environment. These changes require control algorithms capable of operating a multitude of interconnected components. In particular, novel “smart matter” systems will eventually use thousands of embedded, microsize sensors, actuators and processors. In this paper, we propose a new framework for a online, adaptive constrained optimization for distributed embedded applications. In this approach, online optimization problems are decomposed and distributed across the network, and solvers are controlled by an adaptive feedback mechanism that guarantees timely solutions. We also present examples from our experience in implementing smart matter systems to motivate our ideas.
Control Theory
 In Leslie Hogben (Hrsg.), Handbook of Linear Algeba, Kapitel 57, Chapman & Hall/CRC
, 2006
"... Summary and ..."