Obstacle Avoidance Method for Electric Wheelchairs Based on a Multi-Layered Non-Contact Impedance Model

This paper proposes an obstacle avoidance method based on a multi-layered non-contact impedance model for control of the biosignal-based electric wheelchair. The proposed system can calculate a virtual repulsive force before the collision by multi-layered impedance fields covered around it. This system therefore regulates desired path to avoid obstacles in a variety of situations. In the experiments, the mobile robot passed through obstacles smoothly, and could stop emergently to avoid the obstacle in front of the robot owing to virtual forces calculated by the proposed model.This work was supported by JSPS KAKENHI Grant Number JP26330226

DAQL-Enabled Autonomous Vehicle Navigation in Dynamically Changing Environment

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Collision Avoidance for Unmanned Aerial Vehicles

Master'sMASTER OF ENGINEERIN

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic