A robotic intelligent wheelchair system based on obstacle avoidance and navigation functions Mohammed Alshraideh, Basel A. Mahafzah, Saleh Al-Sharaeh & Ziad M. Hawamdeh a Department of Computer Science, King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan b Department of Special Surgery, Faculty of Medicine, The University of Jordan, Amman 11942, Jor...

The obstacle avoidance problem has been a well researched topic by many robotics researchers. We focus on one popular solution to this problem, the Curvature-Velocity Method(CVM). In Simmons’ original paper [5], differentially steered (DS) robots where not considered thoroughly. This paper presents a variation on CVM, specifically designed for DS robots. We show comparable results to that of Si...

Two experiments examined how individuals respond to a restriction presented within an approach versus an avoidance frame. In Study 1, working on a problem-solving task, participants were initially free to choose their strategy, but for a second task were told to change their strategy. The message to change was embedded in either an approach or avoidance frame. When confronted with an avoidance ...

The reinforcing effects of aversive outcomes on avoidance behaviour are well established. However, their influence on perceptual processes is less well explored, especially during the transition from adolescence to adulthood. Using electroencephalography, we examined whether learning to actively or passively avoid harm can modulate early visual responses in adolescents and adults. The task incl...

In this paper a reactive and concurrent control framework for viewpoint control is developed. The viewpoint control task is decomposed into three control objectives namely; obstacle avoidance, visibility and precision. A moving object is tracked in two panoramic sensors using color, and a scalar uncertainty metric of the object position estimate is introduced. Individual control objectives are ...

Learning by observation allows a software agent to learn an expert’s behaviour, by examining the actions the expert performs in response to inputs, without the expert having to explicitly program the agent. Most learning by observation approaches only make use of the current inputs and actions of the expert and ignore any past inputs or actions. This limits the agents to only being able to lear...