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 movi...
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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
The proceedings contain 114 papers. The special focus in this conference is on Infotech and Aerospace. The topics include: Human agent interfaces as a key element for the dialog between human crews and cognitive autom...
ISBN:
(纸本)9781624103384
The proceedings contain 114 papers. The special focus in this conference is on Infotech and Aerospace. The topics include: Human agent interfaces as a key element for the dialog between human crews and cognitive automation;a constrained Markov decision process framework for flight safety assessment and management;signal source localization using partially observable Markov decision processes;adaptive algorithms for autonomous data ferrying in nonstationary environments;adaptive linear quadratic Gaussian optimal control modification for flutter suppression of adaptive wing;performance oriented adaptive architectures with guaranteed bounds;demand-side energy management using an adaptive control strategy for aggregate thermostatic loads;Bayesian modeling for decentralized UAV control and task allocation;consensus based heuristic algorithm for distributed sensor management;autonomous flight path planning for traffic monitoring in wireless sensor networks;a constrained altruistic method for balancing tracking responsibility in a distributed fusion network;decentralized message passing for minimum sensor cover based on belief propagation;coordinating groups of sensing platforms in dynamic, uncertain environments;an intelligent, heuristic path planner for multiple agent unmanned air systems;surveillance for intelligent emergency response robotic aircraft;abnormal orbital event detection, characterization, and prediction;GPS scintillation outage prediction;helicopter mission assignment in disaster relief based on particle swarm optimization;a scented receding horizon approach to the mars surveyor competition and a cascading fuzzy logic controller to the mars surveyor competition.
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.< >
This paper concerns the problem of actively searching for and localizing ground features by a coordinated team of air and ground roboticsensor platforms. The approach taken builds on well known decentralized Data Fus...
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This paper concerns the problem of actively searching for and localizing ground features by a coordinated team of air and ground roboticsensor platforms. The approach taken builds on well known decentralized Data fusion (DDF) methodology. In particular, it brings together established representations developed for identification and linearized estimation problems to jointly address feature detection and localization. This provides transparent and scalable integration of sensor information from air and ground platforms. As in previous studies, an Information-theoretic utility measure and local control strategy drive the robots to uncertainty reducing team configurations. Complementary characteristics in terms of coverage and accuracy are revealed through analysis of the observation uncertainty for air and ground on-board cameras. Implementation results for a detection and localization example indicate the ability of this approach to scalably and efficiently realize such collaborative potential.
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