Legged robots that balance
Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Coupled Oscillator Control of Autonomous Mobile Robots
Autonomous Robots
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Object Tracking using Color Correlogram
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Adaptive Dynamic Walking of a Quadruped Robot on Natural Ground Based on Biological Concepts
International Journal of Robotics Research
Motion planning in the presence of moving obstacles
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Evolutionary trajectory planning for an industrial robot
International Journal of Automation and Computing
Gait transition and modulation in a quadruped robot: A brainstem-like modulation approach
Robotics and Autonomous Systems
CPG modulation for navigation and omnidirectional quadruped locomotion
Robotics and Autonomous Systems
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We present an attractor based dynamics that autonomously generates trajectories with stable timing (limit cycle solutions), stably adapted to changing online sensory information. Autonomous differential equations are used to formulate a dynamical layer with either stable fixed points or a stable limit cycle. A neural competitive dynamics switches between these two regimes according to sensorial context and logical conditions. The corresponding movement states are then converted by simple coordinate transformations and an inverse kinematics controller into spatial positions of a robot arm. Movement initiation and termination is entirely sensor driven. In this article, the dynamic architecture was changed in order to cope with unreliable sensor information by including this information in the vector field. We apply this architecture to generate timed trajectories for a Puma arm which must catch a moving ball before it falls over a table, and return to a reference position thereafter. Sensory information is provided by a camera mounted on the ceiling over the robot. A flexible behavior is achieved. Flexibility means that if the sensorial context changes such that the previously generated sequence is no longer adequate, a new sequence of behaviors, depending on the point at which the changed occurred and adequate to the current situation emerges. The evaluation results illustrate the stability and flexibility properties of the dynamical architecture as well as the robustness of the decision-making mechanism implemented.