The series chopper type power train for EV is proposed for aiming the increase of one battery charge driving range. A motor-test bench and also actual car were constructed, and the proposed power train with SAZZ (Snubber Assisted Zero voltage and Zero current Transition) chopper was experimentally compared between with and without the chopper.
This paper presents a new haptic bilateral control method with vision-based guidance. The vision-based guidance is realized by the visual force compliance controller which can translate the visual information to assistive force. Integration of an imaging modality effectively further consolidates the servoing procedures. Such information needs to be efficiently rendered to the operator at master system. Particularly, the proposed approach provides real-time visualization and force feedback based guidance for the navigation task. The details concerning the method of implementation of this theory will be explained. Finally, the experimental evaluation of the functionality of this visual compliance controller based on force control is described and discussed.
Local path planning considering static and dynamic obstacles for a mobile robot is one of challenging research topics. Conventional local path planning methods generate path candidates by assuming constant velocities for a certain period time. Therefore, path candidates consist of straight line and arc paths. These path candidates are not suitable for dynamic environments and narrow spaces. This paper proposes a novel local path planning method based on dynamic window approach with virtual manipulators (DWV). DWV consists of dynamic window approach (DWA) and virtual manipulator (VM). DWA is the local path planning method that performs obstacle avoidance for static obstacles under robot constraints. DWA also generates straight line and arc path candidates by assuming constant velocities. VM generates velocities of reflective motion by using virtual manipulators and environmental information. DWV generates path candidates by variable velocities modified by VM and predicted positions of static and dynamic obstacles. Therefore, in an environment with dynamic obstacles, the obstacle-avoidable paths which include non-straight line and non-arc paths are generated. The effectiveness of the proposed method was confirmed from simulation and experimental results.
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