Generating Timed Trajectories for Autonomous Robotic Platforms: A Non-Linear Dynamical Systems Approach

Manuela, Cristina; Santos, Peixoto dos

doi:10.5772/4652

Cited by 3 publications

(6 citation statements)

References 22 publications

(10 reference statements)

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“…This model is inspired on the ideas described on [15,37,4,38,32,18,34] and extends current work [28,31,33,29] (in particular [35], where a simulation study was discussed). We apply autonomous differential equations [33] to model the manner how behaviors related to locomotion are programmed in the oscillatory feedback systems of ''central pattern generators'' (CPGs) in the nervous systems [9,42].…”

Section: State Of the Artmentioning

confidence: 98%

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Timed trajectory generation using dynamical systems: Application to a Puma arm

Santos

Ferreira

2009

Robotics and Autonomous Systems

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a b s t r a c tWe 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.

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Section: State Of the Artmentioning

confidence: 98%

“…In [28,31], was implemented in a real vehicle and integrated with other dynamical architectures which do not explicitly parameterize timing requirements. We have also generated temporally coordinated movements among two PUMA arms [35] and among two vision-guided vehicles [29].…”

Section: Introductionmentioning

confidence: 99%

Timed trajectory generation using dynamical systems: Application to a Puma arm

Santos

Ferreira

2009

Robotics and Autonomous Systems

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“…Conversely, the neural weights can be assumed to have relaxed to their corresponding fixed points when analyzing the timing dynamics (adiabatic elimination). The adiabatic elimination of fast behavioral variables reduces the complexity of a complicated behavioral system built up by coupling many dynamical systems (Santos, 2005;Steinhage & Schoner, 1998). By using different time scales one can design the several dynamical systems separately.…”

Section: Neural Dynamicsmentioning

confidence: 99%

Temporal Coordination among Two Vision-Guided Vehicles: A Nonlinear Dynamical Systems Approach

Santos¹,

Ferreira²

2008

Computer Vision

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“…The adiabatic elimination of fast behavioral variables reduces the complexity of a complicated behavioral system built up by coupling many dynamical systems [3], [6]. By using different time scales one can design the several dynamical systems separately.…”

Section: ) Neural Dynamicsmentioning

confidence: 99%

“…In [1], this architecture was implemented in a real vehicle. Further, we have shown that the proposed approach can be integrated with other dynamical architectures which do not explicitly parameterize timing requirements [1], [3].…”

Section: Introductionmentioning

confidence: 99%

Ball Catching by a Puma Arm: a Nonlinear Dynamical Systems Approach

Santos

Ferreira

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We present an attractor based dynamics that autonomously generates temporally discrete movements and movement sequences 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 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. We demonstrate that the implemented decisionmechanism is robust to noisy sensorial information. Further, 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.

show abstract

Generating Timed Trajectories for Autonomous Robotic Platforms: A Non-Linear Dynamical Systems Approach

Cited by 3 publications

References 22 publications

Timed trajectory generation using dynamical systems: Application to a Puma arm

Timed trajectory generation using dynamical systems: Application to a Puma arm

Temporal Coordination among Two Vision-Guided Vehicles: A Nonlinear Dynamical Systems Approach

Ball Catching by a Puma Arm: a Nonlinear Dynamical Systems Approach

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