In this paper we propose a learning mechanism for mobile agent navigation. The agent is controlled by a dynamic set of fuzzy rules, where the rule set is learned using genetic algorithms. The rules are adjusted during...
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Mobile robots can use geometric or topological maps of their environment to navigate reliably. Automatic creation of such maps is still an unrealized goal, especially in environments that have large cyclical structure...
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One of the basic assumptions in the static gait design for a walking robot is that the weight of a leg should be negligible compared to that of body, so that the total gravity center of the robot is not affected by th...
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In this work we propose two behavioraly active policies for attentional control. These policies must act based on a multi-modal sensory feedback. Two approaches are used to derive the policies: the first one follows a...
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A variety of evolutionary algorithms, operating according to Darwinian concepts, have been proposed to approximately solve problems of common engineering applications. Increasingly common applications involve automati...
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This paper presents a novel design method for a non-time based mobile robot controller so that it can track an arbitrary twice differentiable path. The key to the non-time based control method is the introduction of a...
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This paper presents a six degree-of-freedom adaptive controller for autonomous underwater vehicles (AUVs). The proposed control law adopts quaternions to represent attitude errors and thus avoids representation singul...
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Evolutionary robotics is a research area that makes use of the various forms of evolutionary computation (EC) to provide a means of designing robot control systems. The use of EC can reduce development effort and allo...
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We describe a novel approach for shop floor control combining a distributed Multi-Agent structure and computationalintelligence techniques. Shop floor activities are controlled by a network of autonomous agents. Each...
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We describe a novel approach for shop floor control combining a distributed Multi-Agent structure and computationalintelligence techniques. Shop floor activities are controlled by a network of autonomous agents. Each agent makes its decision with a fuzzy algorithm evaluating all the alternative actions with multiple criteria based on real time measures of shop's conditions. A tuning mechanism of the decision algorithm allows agents to adapt themselves to the time varying operating conditions of the manufacturing system. The adaptation process follows a reinforcement learning schema. New agents are periodically created to replace the old ones according to the following strategy: the better is the performance of an agent in his life cycle, the higher is the probability new agents will inherit its decision rules. Preliminary experiments on a detailed simulation model of a Flexible Assembling Systems show the potentialities of the approach and suggest further improvements.
A robust control system is proposed to solve the local control for a multi-chain robotic system formed by tentacle manipulators grasping a common object with hard point contacts. The two-level hierarchical control is ...
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A robust control system is proposed to solve the local control for a multi-chain robotic system formed by tentacle manipulators grasping a common object with hard point contacts. The two-level hierarchical control is adopted. The upper level coordinator gathers all the necessary information to resolve the distribution force. Then, the lower-level local control problem is treated as an open-chain redundant manipulator control problem. The stability and robustness of a class of the fuzzy logic control (FLC) are investigated and a robust FLC is proposed with uncertainties of the load. The fuzzy rules are established. Simulation results are presented and discussed.
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