4,546 research outputs found
An optimal control strategy for collision avoidance of mobile robots in non-stationary environments
An optimal control formulation of the problem of collision avoidance of mobile robots in environments containing moving obstacles is presented. Collision avoidance is guaranteed if the minimum distance between the robot and the objects is nonzero. A nominal trajectory is assumed to be known from off-line planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. Furthermore, time consistency with the nominal plan is desirable. A numerical solution of the optimization problem is obtained. Simulation results verify the value of the proposed strategy
Generalized Regressive Motion: a Visual Cue to Collision
Brains and sensory systems evolved to guide motion. Central to this task is
controlling the approach to stationary obstacles and detecting moving
organisms. Looming has been proposed as the main monocular visual cue for
detecting the approach of other animals and avoiding collisions with stationary
obstacles. Elegant neural mechanisms for looming detection have been found in
the brain of insects and vertebrates. However, looming has not been analyzed in
the context of collisions between two moving animals. We propose an alternative
strategy, Generalized Regressive Motion (GRM), which is consistent with
recently observed behavior in fruit flies. Geometric analysis proves that GRM
is a reliable cue to collision among conspecifics, whereas agent-based modeling
suggests that GRM is a better cue than looming as a means to detect approach,
prevent collisions and maintain mobility
Neural Network Local Navigation of Mobile Robots in a Moving Obstacles Environment
IF AC Intelligent Components and Instruments for Control Applications, Budapest, Hungary, 1994This paper presents a local navigation method based on generalized predictive control. A modified cost function to avoid moving and static obstacles is presented. An Extended Kaiman Filter is proposed to predict the motions of the obstacles. A Neural Network implementation of this method is analysed. Simulation results are shown.Ministerio de Ciencia y Tecnología TAP93-0408Ministerio de Ciencia y Tecnología TAP93-058
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