Mobile Robot Based Target Search In A Smart Home Environment

In recent years,the application of service-oriented intelligent robot to Smart Home has incre-asingly become a hot research issue.Since the scene of the intelligent robot searching people and objects in the home environment will occur frequently,in order to solve this problem,this thesis focuses on the design of a target search algorithm for a mobile robot,and proposes a set of efficient search strategies.In order to accomplish the task of target search in a smart home,the first task is the deve-lopment of a mobile robot.After planning and building the mobile robot hardware and software platform,the basic functions of indoor mobile robots are realized,including robot motion control,sensor management and information fusion,simultaneous localization and mapping,autonomous positioning and coordinate calibration,navigation and obstacle avoidance.Aiming at the problem of mobile robot target search in a smart home environment,this thesis proposes a path planning algorithm based on an improved genetic algorithm and the A*algorithm on the basis of a raster map.Target recognition is based on the deep learning YOLO detection system.The search strategy takes full account of environmental factors and sensor conditions,combining with the probability distribution of the target's location to reduce the total time of the search process.Simulation and experimental results show that the algorithm proposed in this thesis completes the task of searching a target in a short time,and has significant effects in a variety of different situations.The research content in this thesis has very effective application in the elderly care,telemedicine and other daily tasks in a smart home environment.