An intelligent robotic system based on a fuzzy approach

Fukuda, Toshio; Kubota, Naoyuki

doi:10.1109/5.784220

Cited by 159 publications

(36 citation statements)

References 41 publications

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“…It is multi-modal (different fuzzy rule sets and/or membership functions may have similar performance) and deceptive, since a little modification may cause huge effects on the performance of each system [5]. There is much work reported in the literature on designing fuzzy controllers using GA [2,7,6,7,13,18,19,20,21]. However virtually most of this work was undertaken using simulation as in conventional GA, it takes a large number of iterations to develop a good controller.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

“…Consequently the agent must be able to perceive the environment, make decisions, represent sensed data, acquire knowledge, and infer rules concerning the environment. Agents that can acquire and usefully apply knowledge or skill are often called Intelligent, these agents receive a task from a human operator and must accomplish the task in the available workspace [7].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Hagras

Sobh

2002

Information Sciences

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The control of autonomous intelligent robotic agent operating in unstructured changing environments includes many objective difficulties. One major difficulty concerns the characteristics of the environment that the agent should operate in. In unstructured and changing environments the inconsistency of the terrain, the irregularity of the product and the open nature of the working environment result in complex problems of identification, sensing and control.Problems can range from the effects of varying environmental conditions on the robot sensors and traction performance through to the need to deal with the presence of unexpected situations. Another major challenge is the large amounts of uncertainty that characterises real-world environments. On the one hand, it is not possible to have exact and complete prior knowledge of these environments. On the other hand, knowledge acquired through sensing is affected by uncertainty and imprecision. The quality of sensor information is influenced by sensor noise, the limited field of view, the conditions of observation, and the inherent difficulty of the perceptual interpretation process.Because environments and users of systems continuously change, robotic agents have to be adaptive. Intelligence helps because it gives systems the capacity to adapt more rapidly to environmental changes or to handle much more complex functions. In his paper we introduce this special issue and introduce the difficulty robots are facing in unstructured environments and how learning and computational intelligence can help the robots to adapt and give them the necessary intelligence they to face the challenges they encounter in their environments.

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Section: Evolutionary Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Hagras

Sobh

2002

Information Sciences

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“…solution is eliminated ("Delete least fitness" selection strategy), and is replaced with the candidate solution generated by the crossover and the mutation. We use the elitist crossover and adaptive mutation [26]. The elitist crossover randomly selects one individual and generates an individual by combining genetic information from the randomly selected individual and the best individual.…”

Section: A Human Face Detectionmentioning

confidence: 99%

Visual Perception for Emotion-Based Communication of a Partner Robot

Kubota

Liu

Mori

2009

KANSEI Engineering International

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This paper discusses natural communication based on an emotional model for partner robots based on visual perception. The partner robots should have various capabilities of perceiving, acting, communicating, and surviving through physically and emotionally interacting with a human. First of all, we discuss the role of an emotional model for a partner robot. Next, we propose the concept of natural communication with an emotional model. Partner robots performs utterances based on the results of the emotional model. Furthermore, this paper proposes a method for extracting human facial landmarks to realize natural communication for partner robots. Evolutionary computation is used for human face detection and human tracking based on adaptive color template matching. A temporary template pattern is updated according to the search result of the previous image. After constructing a normalized human face image, the positions and features of facial landmarks are extracted. Finally, we show experimental results of the emotional communication based on the visual perception.

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“…The studies on cars and robots become popular as the demand for these systems increases [3]- [5]. Some researches treat avoidance problems as the formation control which requires the cooperative information control [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Experiment of Collision Avoidance Control Law with Information Amount Feedback Using Vehicle Model

Higuchi

Kokubu

Ueno

2013

SICE Journal of Control, Measurement, and System Integration

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: This paper is on the experiment of the collision avoidance control law with information amount feedback using a vehicle model. The collision avoidance is a key technology for future mass transportation systems. The amount of information obtained by the evader is treated as a physical value for the collision avoidance feedback control. This will lower the risk of collision when information of the environment cannot be obtained due to various reasons and when the intruder is coming in from out-of-sight. This paper demonstrates the effect of collision avoidance control with information amount feedback using a vehicle model. The results show that the control law has the ability to lower the risk of the collision and works in the actual environment for collision avoidance with similar motion with that of humans.

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An intelligent robotic system based on a fuzzy approach

Cited by 159 publications

References 41 publications

Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Visual Perception for Emotion-Based Communication of a Partner Robot

Experiment of Collision Avoidance Control Law with Information Amount Feedback Using Vehicle Model

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