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Resource-based action planning for multi-agent systems

Resource-based action planning for multi-agent systems

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conference on sensor fusion amd decentralized control in robotic systems

作者： Freund, E Hoffmann, K Rossmann, J Univ Dortmund Inst Robot Res D-44227 Dortmund Germany

ISBN: (纸本)0819429848

With the use of multi-robot-systems new chances and perspectives are revealed for industrial, space- and underwater-applications. At the IRF, a versatile multi-robot-control, which fully exploits the inherent flexibility of a multi-robot-system, has been developed. In order to guarantee optimized system-throughput and increased autonomy, the system builds on a new resource-based action planning approach to coordinate the different robot manipulators and other automation components in the workcell. An important issue of the realized action planning component to be applicable to real world problems is that it is realized as an integral part of the hierarchical multi-robot control structure IRCS (Intelligent Robot control System). The IRCS is the multi-robot control that was chosen by the German Space Agency (DLR) for major space automation projects. In this structure the resource-based action planning component is tightly integrated with components for coordinated task execution and collision avoidance to guarantee save operation of all agents in the multi-robot system. As the action planning component "understands" task descriptions on a high level of abstraction it is also the perfect counterpart for a Projective Virtual Reality (VR) system. The paper will describe the mechanism of resource based action planning, the practical experiences gained from the implementation for the IRCS as well as its services to support VR-based man machine interfaces.

关键词： resource-based action planning action pattern net multistage action planning online model expansion intelligent multi-robot-control exploitation of flexibility virtual reality user friendliness increased autonomy and safety

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Models of basal ganglia and cerebellum for sensorimotor integration and predictive control

Models of basal ganglia and cerebellum for sensorimotor inte...

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conference on sensor fusion and decentralized control in robotic systems iii

作者： Jabri, MA Huang, J Coenen, OJMD Sejnowski, TJ Univ Sydney Sch Elect & Informat Engn Comp Engn Lab Sydney NSW 2006 Australia

ISBN: (纸本)0819438618

This paper presents a sensorimotor architecture integrating computational models of a cerebellum and a basal ganglia and operating on a microrobot. The computational models enable a microrobot to learn to track a moving object and anticipate future positions using a CCD camera. The architecture features pre-processing modules for coordinate transformation and instantaneous orientation extraction. Learning of motor control is implemented using predictive Hebbian reinforcement-learning algorithm in the basal ganglia model. Learning of sensory predictions makes use of a combination of long-term depression (LTD) and long-term potentiation (LTP) adaptation rules within the cerebellum model. The basal ganglia model uses the visual inputs to develop sensorimotor mapping for motor control, while the cerebellum module uses robot orientation and world-coordinate transformed inputs to predict the location of the moving object in a robot centered coordinate system. We propose several hypotheses about the functional role of cell, populations in the cerebellum and argue that mossy fiber projections to the deep cerebellar nucleus (DCN) could play a coordinate ransformation role and act as gain fields. We propose that such transformation could be learnt early in the brain development stages and could be guided by the activity of the climbing fibers. Proprioceptor mossy fibers projecting to the DCN and providing robot orientation with respect to a reference system could be involved in this case. Other mossy fibers carrying visual sensory input provide visual patterns to the granule cells. The combined activities of the granule and the Purkinje cells store spatial representations of the target patterns. The combinations of mossy and Purkinje projections to the DCN provide a prediction of the location of the moving target taking into consideration the robot orientation. Results of lesion simulations based on our model show degradations similar to those reported in cerebellar lesion st

关键词： Cerebellum Visualization sensors Basal ganglia Neurons Light emitting diodes Visual process modeling Cameras Thalamus Brain

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Auto-agent: A behavior-based architecture for mobile navigation

Auto-agent: A behavior-based architecture for mobile navigat...

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conference on sensor fusion amd decentralized control in robotic systems

作者： Hwang, KS Lu, MY Natl Chung Cheng Univ Dept Elect Engn Chai Yi Taiwan

ISBN: (纸本)0819429848

The design and construction of mobile robots is as much an art as a science. Ire hardware architecture, researchers tend to construct a low-cost and reliable platform which equips with various sensory system for sensing the change of the environment to offer useful information to the navigation system. An autonomous navigation system plays a role in an mobile robot as the brain in human being, It generates action command according to those sensory data from the perception system to direct the mobile robot to go to desired positions or accomplish useful tasks without human intervention in real-world, An important problem hi autonomous navigation is the need to cope with the large amount of uncertainty that is inherent of natural environment. Therefore the development of techniques for autonomous navigation in real-world environments constitutes one uf the major trends in the current research on robotics. Inspired with the concept of software agents, reactive control and behavior-based control a modular architecture called Ante-agent, for mobile navigation is proposed, The main characteristic of Auto-agent is as following: Behavioral agents cooperate by means of communicating with other behavioral agents intermittently to achieve their local goal and the goals of the community as a whole because nea one individually has sufficient competence, resources and information to solve the entire probit nl. Auto-agent gains advantages from the characteristics of distributed system, it offers the possibility to find an acceptable solution with a reasonable time and complexity range, Besides, the modular structure iu convenient nt for an engineer to construct a new behavioral agent and to add it into Auto-agent.

关键词： behavior-based navigation system reactive control mobile robot

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A modified reactive control framework for cooperative mobile robots

A modified reactive control framework for cooperative mobile...

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conference on sensor fusion and decentralized control in Autonomous robotic systems

作者： Salido, J Dolan, JM Hampshire, J Khosla, P Robotics Institute Carnegie Mellon Univ. Pittsburgh PA 15213-3891 United States Dept. of Electrical and Computer Engineering Carnegie Mellon Univ. Pittsburgh PA 15213-3890 United States Castilla-La Mancha Univ.

ISBN: (纸本)0819426415

An important class of robotic applications potentially involves multiple, cooperating robots: security or military surveillance, rescue, mining, etc. One of the main challenges in this area is effective cooperative control: how does one determine and orchestrate individual robot behaviors which result in a desired group behavior? Cognitive (planning) approaches allow for explicit coordination between robots, but suffer from high computational demands and a need for a priori, detailed world models. Purely reactive approaches such as that of Brooks are efficient, but lack a mechanism for global control and learning. Neither approach by itself provides a formalism capable of a sufficiently rapid and rich range of cooperative behaviors. Although we accept the usefulness of the reactive paradigm in building up complex behaviors from simple ones, we seek to extend and modify it in several ways. First, rather than restricting primitive behaviors to fixed input-output relationships, we include memory and learning through feedback adaptation of behaviors. Second, rather than a fixed priority of behaviors, our priorities are implicity they vary depending on environmental stimuli. Finally, we scale this modified reactive architecture to apply not only for an individual robot, but also at the level of multiple cooperating robots: at this level, individual robots are like individual behaviors which combine to achieve a desired aggregate behavior. In this paper, we describe our proposed architecture and its current implementation. The application of particular interest to us is the control of a team of mobile robots cooperating to perform area surveillance and target acquisition and tracking.

关键词： cooperative mobile robots social behavior reinforcement learning intelligence

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A vision-guided, semi-autonomous system applied to a robotic coating application

A vision-guided, semi-autonomous system applied to a robotic...

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conference on sensor fusion and decentralized control in Autonomous robotic systems

作者： Seelinger, MJ Robinson, M Dieck, Z Skaar, SB University of Notre Dame Department of Aerospace and Mechanical Engineering Fitzpatrick Hall of Engineering Notre Dame IN 46556 United States

ISBN: (纸本)0819426415

This paper describes a new semi-autonomous, calibration-free system which integrates a user-friendly graphical interface, several cameras, a laser pointer mounted on a two-axis pan/tilt unit, and a six degree-of-freedom robot. The details of the system will be discussed in reference to the problem of coating a workpiece of unknown geometry that is positioned arbitrarily with respect to the robot and the vision sensors. The remote user specifies the region of the workpiece that is to be coated simply by ''pointing'' and ''clicking'' on the region of interest as it appears in a single image on the user's computer monitor. By means of a simple and robust control strategy, the laser pointer mounted on the pan/tilt unit is autonomously actuated to a user-specified number of approximate positions in the region of the surface of interest. This is used to create compatible maneuver objectives in the participating vision sensors. Then, using the method of camera-space manipulation, the robot is controlled to make several passes across the region. Regardless of the geometry of the workpiece, the manipulated nozzle always remains perpendicular to the surface at a user-specified distance from the surface, Upon completion of the coating process, the laser pointer is again actuated to pass through a specified number of points on the :new surface. This information is used to make a very precise inference of the thickness of the build-up of the coat that has been applied. If the coat is not sufficiently thick, the robot makes more passes as required. The paper also presents experimental results of the high accuracy of position and orientation control of the manipulated tool, as well as the depth inference of the surface coat applied.

关键词： camera-space manipulation robot vision supervisory control inspection graphical user interface

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A framework for coordinating multiple robots in cooperative manipulation tasks

A framework for coordinating multiple robots in cooperative ...

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sensor fusion and decentralized control in robotic systems IV conference

作者： Chaimowiez, L Kumar, V Campos, M Univ Fed Minas Gerais DCC VERLab BR-31270010 Belo Horizonte MG Brazil

ISBN: (纸本)0819442992

In this paper, we describe a framework for coordinating multiple robots in the execution of a cooperative manipulation task. The coordination is distributed among the robots that use modular, hybrid controllers in order to execute the task. Different plans and models of the environment are built using a mix of global and local information and can be shared by the team members using wireless communication. Our framework uses models and metrics of computation, control, sensing and information in order to dynamically assign roles to the robots, and define the most suitable control hierarchy given the requirements of the task and characteristics of the robots. To test our framework, we have developed a object oriented simulator that allows the user to create different types of robots, to define different environments and to specify various types of control algorithms and communication protocols. Using the simulator, we have been able to test the execution of this task in different scenarios with different numbers of robots. We can verify the efficacy of the robot controllers, the effects of different parameters on the system performance and the ability of the robots to adapt to changes in the environment. Results from experiments with two real robots and with the simulator are used to explore the multi-robot coordination in a cooperative manipulation task.

关键词： cooperative robotics distributed control

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Ceramic Engineering and Science Proceedings: 63rd conference on Glass Problems

Ceramic Engineering and Science Proceedings: 63rd Conference...

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sensor fusion and decentralized control in robotic systems IV

This Volume 24 of the conference proceedings contains 18 papers. Topics discussed include glass forming, glass melting, recycling and glass production.

关键词： Glass

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Application of a model of instrumental conditioning to mobile robot control

Application of a model of instrumental conditioning to mobil...

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conference on sensor fusion and decentralized control in Autonomous robotic systems

作者： Saksida, LM Touretzky, DS Carnegie Mellon Univ. (United States)

ISBN: (纸本)0819426415

Instrumental conditioning is a psychological process whereby an animal learns to associate its actions with their consequences. This type of learning is exploited in animal training techniques such as ''shaping by successive approximations'', which enables trainers to gradually adjust the animal's behavior by giving strategically timed reinforcements. While this is similar in principle to reinforcement learning, the real phenomenon includes many subtle effects not considered in the machine learning literature, In addition, a good deal of domain information is utilized by an animal learning a new task;it does not start from scratch every time it learns a new behavior. For these reasons, it is not surprising that mobile robot learning algorithms have yet to approach the sophistication and robustness of animal learning. A serious attempt to model instrumental learning could prove fruitful for improving machine learning techniques. In the present paper, we develop a computational theory of shaping at a level appropriate for controlling mobile robots. The theory is based on a series of mechanisms for ''behavior editing'', in which pre-existing behaviors, either innate or previously learned, can be dramatically changed in magnitude, shifted in direction, or otherwise manipulated so as to produce new behavioral routines. We have implemented our theory on Amelia, an RWI B21 mobile robot equipped with a gripper and color video camera. We provide results from training Amelia on several tasks, all of which were constructed as variations of one innate behavior, object-pursuit.

关键词： autonomous mobile robots instrumental learning operant conditioning reinforcement learning shaping

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Coordinated perception by teams of aerial and ground robots

Coordinated perception by teams of aerial and ground robots

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conference on Mobile Robots XVII

作者： Grocholsky, B Swaminathan, R Kumar, V Taylor, C Pappas, G Univ Penn GRASP Lab Philadelphia PA 19104 USA

ISBN: (纸本)0819455628

Air and ground vehicles exhibit complementary capabilities and characteristics as robotic sensor platforms. Fixed wing aircraft offer broad field of view and rapid coverage of search areas. However, minimum operating airspeed and altitude limits, combined with attitude uncertainty, place a lower limit on their ability to detect and localize ground features. Ground vehicles on the other hand offer high resolution sensing over relatively short ranges with the disadvantaue of slow coverage. This paper presents a decentralized architecture and solution methodology for seamlessly realizing the collaborative potential of air and ground robotic sensor platforms. We provide a framework based on an established approach to the underlying sensor fusion problem. This provides transparent integration of information from heterogeneous sources. An information-theoretic utility measure captures the task objective and robot inter-dependencies. A simple distributed solution mechanism is employed to determine team member sensing trajectories subject to the constraints of individual vehicle and sensor sub-systems. The architecture is applied to a mission involving searching for and localizing an unknown number of targets in an user specified search area. Results for a team of two fixed wing UAVs and two all terrain UGVs equipped with vision sensors are presented.

关键词： heterogeneous robot teams active sensing sensor fusion coordinated control

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Discrete time neural network synthesis using interaction activation functions

Discrete time neural network synthesis using interaction act...

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conference on sensor fusion and Distributed robotic Agents

作者： Novakovic, BM Univ. of Zagreb (Croatia)

ISBN: (纸本)0819423076

A new very fast algorithm for synthesis of discrete-time neural networks (DTNN) is proposed. For this purpose the following concepts are employed: (i) introduction of interaction activation functions, (ii) time-varying DTNN weights distribution, (iii) time-discrete domain synthesis and (iiii) one-step learning iteration approach.. The proposed DTNN synthesis procedure is useful for applications to identification and control of nonlinear, very fast, dynamical systems. In this sense a DTNN for a nonlinear robot control is designed. As the contributions of the paper, the following items can be cited. A nonlinear, discrete-time state representation of a neural structure was proposed for one-step learning. Within the structure, interaction activation functions are introduced which can be combined with input and output activation functions. A new very fast algorithm for one step learning of DTNN is introduced, where interaction activation functions are employed. The functionality of the proposed DTNN structure was demonstrated with the numerical example where a DTNN model for a nonlinear robot control is designed. This DTNN model is trained to imitate a nonlinear robot control algorithm, based on the dynamics of the full robot model of RRTR-structure. The simulation results show the satisfactory performances of the trained DTNN model.

关键词： recurrent neural networks interaction activation functions time-discrete domain synthesis one-step learning nonlinear dynamical systems nonlinear robot control

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