Two issues that are fundamental to developing autonomous intelligent robots, namely rudimentary learning capability and dexterous manipulation, are examined. A powerful neural learning formalism is introduced for addressing a large class of nonlinear mapping problems, including redundant manipulator inverse kinematics, commonly encountered during the design of real-time adaptive control mechanisms. Artificial neural networks with terminal attractor dynamics are used. The rapid network convergence resulting from the infinite local stability of these attractors allows the development of fast neural learning algorithms. Approaches to manipulator inverse kinematics are reviewed, the neurodynamics model is discussed, and the neural learning algorithm is presented. >

Neutral learning of constrained nonlinear transformations

A hybrid control architecture combining behavior based reactive navigation and model based environment classification has been developed. It is also hybrid in the sense that both competitive coordination and cooperative coordination are used for the BBC (Behavior Based Control) part. The contributions are as follows. First, a Neural Network (NN) in charge of environment classification has been developed based on 16 prototypes of topological maps roughly describing various local navigation environments. This environment classification NN not only enables the navigator to avoid local minimum points but also eliminates the requirement for prior detailed modeling of the environment since it needs to memorize only “rough” information on local environments encountered along the way that might be sufficient for navigation. Next, an NN based reactive behavior controller will be trained to learn human steering commands for each of the 16 prototype local environments. Third, the modified potential field (MPF) method obtained by adding the free space vector as the third component is used to select a particular reactive behavior in conjunction with the classification NN. Finally, a hybrid control architecture integrating all three of these concepts was developed. It avoids local minimum traps as well as solves the problems of poor obstacle clearance or oscillation. It is robust against sensor noise and adaptive to dynamic environments. This hybrid architecture is also amenable to easy addition of new behaviors due to the modularity of the BBC architecture. The effectiveness of the proposed architecture has been verified through both computer simulation and an actual robot called MORIS (MObile Robot as an Intelligent System).

Hybrid Control for Autonomous Mobile Robot Navigation Using Neural Network Based Behavior Modules and Environment Classification

This paper proposes to automate the generation of shellfish exploitation plans, which are elaborated by Galician extracting entities. For achieving this objective a CBR-BDI agent will be used. This agent will adapt the exploitation plans to the environmental characteristics of each school of shellfish. This kind of agents develops its activity into changing and dynamic environments, so the reasoning model that they include must be emphasised. The agent reasoning model is guided by the phases of the CBR life cycle, using different technologies for each phase. The use of an adaptative neuro-fuzzy inference system in the reuse phase must be highlighted.

A reasoning model for CBR―BDI agents using an adaptable fuzzy inference system

A method and an apparatus for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network has been applied to the highly degenerative inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 109 ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at ≈10 μsec.

Adaptive neuron model--an architecture for the rapid learning of nonlinear topological transformations

This paper proposes to automate the generation of shellfish exploitation plans, which are elaborated by Galician extracting entities For achieving this objective a CBR-BDI agent will be used This agent will adapt the exploitation plans to the environmental characteristics of each school of shellfish This kind of agents develops its activity into changing and dynamic environments, so the reasoning model that they include must be emphasised The agent reasoning model is guided by the phases of the CBR life cycle, using different technologies for each phase The use of an adaptative neuro-fuzzy inference system in the reuse phase must be highlighted

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R. Rawel

Papers

Neural networks for robotic control