This work presents a biologically-inspired coordination model which associates motor actions with visual stimuli. The model is introduced and explained, and navigation experiments are reported that verify the implemented visual-motor system. Experiments demonstrate that the system can be trained to solve navigation problems consisting in moving around a 3D object to reach a specific location ba...

This paper presents the application of stratified motion planning to the robotic manipulation problem. Although the manipulation problem is a subclass of applications for stratified motion planning, the method present is general in that it is formulation in a manner independent of the object surface geometry or the kinematics of the “fingers” of the robot. The theoretical development of the met...

Achieving eecient data fusion through integration of sensory perception control and sensor fusion .

Article history: Received 23 October 2009 Received in revised form 20 July 2010 Accepted 4 August 2010 Available online 10 August 2010

Many robot motion planning problems can be described as a combination of motion through relatively sparsely filled regions of configuration space and motion through tighter passages. Sample-based planners perform very effectively everywhere but in the tight passages. In this paper, we provide a method for parametrically describing workspace arrangements that are difficult for planners, and then...

The goal of this project was to develop a framework for robotic planning and execution that provides a continuum of adaptability with respect to model incompleteness, model error, and sensing error. For example, dividing robot motion into gross-motion planning, fine-motion planning, and sensor-augmented control had yielded productive research and solutions to individual problems. Unfortunately,...

Sampling-based approaches such as Probabilistic Roadmaps and Rapidly-exploring Random Trees are very popular in motion planning. Monte Carlo Random Walks (MRW) are a quite different sampling method. They were implemented in the Arvand family of planners, which have been successful in classical planning with its discrete state spaces and actions. The work described here develops an MRW approach ...