Human motion is a daily and rhythmic activity. The exoskeleton concept is a very positive scientific approach for human rehabilitation in case of lower limb impairment. Although the exoskeleton shows potential, it is not yet applied extensively in clinical rehabilitation. In this research, a fuzzy based control algorithm is proposed for lower limb exoskeletons during sit-to-stand and stand-to-sit movements. Surface electromyograms (EMGs) are acquired from the vastus lateralis muscle using a wearable EMG sensor. The resultant acceleration angle along the z-axis is determined from a kinematics sensor. Twenty volunteers were chosen to perform the experiments. The whole experiment was accomplished in two phases. In the first phase, acceleration angles and EMG data were acquired from the volunteers during both sit-to-stand and stand-to-sit motions. During sit-to-stand movements, the average acceleration angle at activation was 11°–48° and the EMG varied from −0.19 mV to +0.19 mV. On the other hand, during stand-to-sit movements, the average acceleration angle was found to be 57.5°–108° at the activation point and the EMG varied from −0.32 mV to +0.32 mV. In the second phase, a fuzzy controller was designed from the experimental data. The controller was tested and validated with both offline and real time data using LabVIEW.

Localization and mapping are very important for safe movement of robots. One possible way to assist with this functionality is to use laser scan matching. This paper describes a method to implement this functionality. It is based on well-known random sampling and consensus (RANSAC) and iterative closest point (ICP). The proposed algorithm belongs to the class of point to point scan matching approach with its matching criteria rule. The performance of the proposed algorithm is examined in real environment and found applicable in real-time application