Artificial potential field is a powerful method of navigation for mobile robots in a cluttered environment. Despite the advantages that it offers, this method is not free from local minima and oscillation problems. This paper addresses the oscillation problem near the obstacles and in narrow passages. A comparative study has been made between the traditional gradient descent technique and second order methods and it has been shown that the Levenberg-Marquardt algorithm improves upon the oscillation problem and generates smoother trajectories in fewer steps.

Aim of this paper is to develop an EMG based biofeedback system using a virtual reality platform which will help in gait rehabilitation. A low power multichannel EMG acquisition unit has been developed to acquire EMG of six different muscles of the lower limb. EMG from different channels are fused using Bayesian fusion technique and spurious data has been discarded. From the fused EMG data, we calculate different gait parameters like stride time, gait phase etc. Joint trajectory during a gait cycle is obtained, digitized and combined with the gait parameters acquired from EMG. Together they are fed to a VR human model. Just like a person walks, the same EMG and trajectory data being fed to the model, it walks too mimicking the gait of the user, with the same speed, thus providing biofeedback to the user.The system has massive application in gait rehabilitation for poststroke patients, people suffering from cerebral palsy and other neuro muscular gait defects, amputees etc.

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Summary This paper presents an integrated optimal control framework for velocity and steering control of an autonomous pursuit vehicle, where the control objectives satisfy the requirements of collision avoidance and moving target tracking. A distinctive feature of the proposed velocity and steering control is the application of logarithmic penalty functions to both. The control barrier imposed by logarithmic function provides a unique tool in computing a balanced trajectory with optimal tracking error, control effort and safety margin. Trajectories compliant with the safety regulations for autonomous driving have been planned based on estimated intention of the target and the obstacles. Effects of the controller weights have been extensively simulated to assess the performance of the proposed strategy in a variety of dynamic situations. The controller has been validated on a real-life robot by using a shrinking horizon control policy for iterative optimisation.

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