Article

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

Soft Computing (Impact Factor: 1.27). 05/2008; 12(7):715-720. DOI: 10.1007/s00500-007-0245-0
Source: DBLP

ABSTRACT

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning
to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories
if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors
without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle
to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as
the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based
on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.

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    • "learning theory and hyperplane clustering-based techniques [4] [5] [6] [7]. The underlying idea of such techniques received in the literature many acknowledgments as, for example, in the case of theoretical models [8] [9] [10] [11] [12] [13] [14] as well as applications to specific fields [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]. "
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    ABSTRACT: Adaptive neurofuzzy inference systems (ANFIS) represent an efficient technique for the solution of function approximation problems. When numerical samples are available in this regard, the synthesis of ANFIS networks can be carried out exploiting clustering algorithms. Starting from a hyperplane clustering synthesis in the joint input-output space, a computationally efficient optimization of ANFIS networks is proposed in this paper. It is based on a hierarchical constructive procedure, by which the number of rules is progressively increased and the optimal one is automatically determined on the basis of learning theory in order to maximize the generalization capability of the resulting ANFIS network. Extensive computer simulations prove the validity of the proposed algorithm and show a favorable comparison with other well-established techniques.
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    • "Particularly, a fishlike underwater robot is one of these categories. Our lab introduced a simple fishlike robot in 2005[3], and has improved and added new functions in various manners[4] [5] [6]. To confirm their effectiveness, our constructed fish robots have been tested in a tank for user interaction as well as collision avoidance, maneuverability, control performance, posture maintenance, path design, and data "
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    • "Our lab introduced a simple fishlike robot in 2005[3], and improved and added new functions in various manners shapes[4] [5] [6] [7]. To confirm their effectiveness, our constructed fish robots have been tested in tanks and pools for user interactions as well as collision avoidance, maneuverability, control performance, posture maintenance, path design, and data communication. "
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    ABSTRACT: Development of bio-mimetic entertainment dolphin robots that act like real dolphins in terms of autonomous swimming and human-dolphin interactions are introduced. Body structures, sensors and actuators, governing microcontroller boards, swimming and interaction features are described for a typical entertainment dolphin robot. Actions of mouth-opening, tail splash or water blow through a spout hole are the typical responses of interaction when touch sensors on its body detect users' demand. A pair of microphones as the ears of a dolphin robot, in order to improve the entertainment dolphin robot's ability to interact with people, is used to estimate the peak sound directions from surrounding viewers. Dolphin robots should turn towards people who demand to interact with them, while swimming autonomously.
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