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-freed...
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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.
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 co...
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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.
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 ''shapin...
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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.
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-varyin...
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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.
The authors address the development of a coherent framework suitable for the treatment of the heterogeneous multisensorfusion problem, in the context of a robotic environment. The analysis, is based on a geometric de...
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The authors address the development of a coherent framework suitable for the treatment of the heterogeneous multisensorfusion problem, in the context of a robotic environment. The analysis, is based on a geometric description of the robotic environment, and concentrates on parameterizable features of the assumed rigid dynamic objects of interest present in the environment. A quantitative representation of a sensor's inherent ability to extract pertinent information from the environment is stressed. A highly efficient, flexible, and fault-tolerant, decentralizedsensorfusion architecture based on a linear information type structure is presented and compared with previous work in this area. The inclusion of techniques to cope with generalized spatial and temporal uncollocation, along with conclusive discussions on the most appropriate level in the fusion structure for these alignment procedures to be performed, represent the principal contribution.< >
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