Evolving cooperative neural agents for controlling vision guided mobile robots

Chang, Oscar

doi:10.1109/ukricis.2010.5898127

Cited by 10 publications

(10 citation statements)

References 8 publications

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“…We use the n-flop as an integrating, modulable behavior initiating agent, modulated with compressed image information coming from others agents. In previous experiments this arrangement showed encouraging results [11].…”

Section: Behavior Initiating Agentsmentioning

confidence: 64%

“…End-ofrun information which come from the buffers is fed back toward the n-flops so that in free running conditions the eye improves its random search (figure 2). Considerations about this search are given in [11]. …”

Section: A the Behavior Initiating Agentmentioning

confidence: 99%

“…In the previous work, the selection of the inter agent weights was done with simulated evolution [11]. For this paper reverse engineering is used and the robotics eye is manually tuned until it reaches its top working capacity.…”

Section: Agents Cooperation: the Modulation Processmentioning

confidence: 99%

“…In [10] the behavior-release mechanism of the basal ganglia was used in a robot controller. In [11] it was experimentally demonstrate that behavior initiating agents based upon ANN and working in collaboration with other image processing agents, produce highly proactive vision guided robots.…”

Section: Introductionmentioning

confidence: 99%

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A robotic eye controller based on cooperative neural agents

Chang

Campoy

Martínez

et al. 2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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Abstract-A neural behavior initiating agent (BIA) is proposed to integrate relevant compressed image information coming from others cooperating and specialized neural agents. Using this arrangement the problem of tracking and recognizing a moving icon has been solved by partitioning it into three simpler and separated tasks. Neural modules associated to those tasks proved to be easier to train and show a good general performance. The obtained neural controller can handle spurious images and solve an acute image related task in a dynamical environment. Under prolonged dead-lock conditions the controller shows traces of genuine spontaneity. The overall performance has been tested using a pan and tilt camera platform and real images taken from several objects, showing the good tracking results discussed in the paper.

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Section: Behavior Initiating Agentsmentioning

confidence: 64%

Section: A the Behavior Initiating Agentmentioning

confidence: 99%

Section: Agents Cooperation: the Modulation Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A robotic eye controller based on cooperative neural agents

Chang

Campoy

Martínez

et al. 2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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“…Actually, vision has been used in numerous robotic applications to successfully achieve a task (e.g. obstacle avoidance for navigation [12], [13], [14], [15], human recognition for Human-Robot Interaction [16], [17], activity recognition for cooperative behaviour [18], [19], [20] and object identification for manipulation [21], [22], [23], to name only a few). However, despite significant achievements, the problem of detecting and recognising objects efficiently and accurately still remains a scientific challenge when real scenes are considered.…”

Section: Introductionmentioning

confidence: 99%

Object Detection and Recognition for Assistive Robots: Experimentation and Implementation

Martínez-Martín

Pobil

2017

IEEE Robot. Automat. Mag.

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Abstract-Technological advances are currently being directed to assist the human population in performing ordinary tasks in everyday settings. In this context, a key issue is the interaction with objects of varying size, shape and degree of mobility. Consequently, autonomous assistive robots must be provided with the ability to process visual data in real time so that they can react adequately for quickly adapting to changes in the environment. Reliable object detection and recognition is usually a necessary early step to achieve this goal. In spite of significant research achievements, this issue still remains a challenge when real-life scenarios are considered. In this paper, we present a vision system for assistive robots that is able to detect and recognise objects from a visual input in ordinary environments in real time. The system computes colour, motion and shape cues combining them in a probabilistic manner to accurately achieve object detection and recognition, taking some inspiration from vision science. In addition, with the purpose of processing the input visual data in real-time, a Graphical Processing Unit (GPU) has been employed. The presented approach has been implemented and evaluated on a humanoid robot torso located at realistic scenarios. For further experimental validation, a public image repository for object recognition has been used, allowing a quantitative comparison with respect to other state-of-the-art techniques when real-world scenes are considered. Finally, a temporal analysis of the performance is provided with respect to image resolution and number of target objects in the scene.

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Self-programming Robots Boosted by Neural Agents

Chang

2018

Brain Informatics

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Evolving cooperative neural agents for controlling vision guided mobile robots

Cited by 10 publications

References 8 publications

A robotic eye controller based on cooperative neural agents

A robotic eye controller based on cooperative neural agents

Object Detection and Recognition for Assistive Robots: Experimentation and Implementation

Self-programming Robots Boosted by Neural Agents

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