Research On Localization And Motion Planning For A Humanoid Robot In Complex Indoor Environment

It is the inevitable trend that a humanoid robot enters the complex indoor environment,and serves the daily life of human being.To serve the people autonomously,location and motion planning are essential technologies for a humanoid robot.Current research is focused on localization and motion planning in a simple environment,while few of them consider those two problems in complex indoor environment.Although there are some localization and motion planning methods for wheeled robots,due to the special characteristics of humanoid robot,most methods for wheeled robot are not suitable for humanoid robot,and there are still many problems to be solved.Supported by National Natural Science Foundation of China “Research on simultaneous localization and 3D cognitive map-building for a humanoid robot”,the purpose of this research is to improve the autonomous ability of humanoid robot.It mainly concerns about the problems of localization and motion planning for humanoid robot in complex indoor environment.Key technologies involved are systematically and deeply studied.It mainly includes environment perception,location and motion planning.Firstly,the problem of indoor mapping for a humanoid robot is studied.It proposed a hybrid map method for mapping an indoor environment.Global topological map is constructed by natural landmarks,and 2D and 3D maps are built by different sensors for local metric layer.The 2D map is established by an laser rangefinder.And the 3D map is established by a RGB-D camera.In addition,the auxiliary semantic layer is established based on QR code.And a unified framework of semantic-topological-metric hybrid map is set up,which can meet the requirements of indoor positioning and navigation for humanoid robots.Secondly,the localization problem of a humanoid robot is studied.In this paper,the localization problem is deeply studied in two cases: the environment map is known and unknown.A global positioning method based on PNP is proposed for topological map,and a positioning method based on KLD-Monte Carlo is presented for local metric map.To deal with the unknown environment,a method named Sliding Window Extended Kalman Filter is proposed.Those three methods can be applied to global and local localization for humanoid robot,which can obtain better positioning results.Thirdly,the problem of humanoid robot climbing stairs and slopes is studied.A motion planning method based on the improved NSGA-II is proposed.A seven-link model of humanoid robot is established,and motion model of climbing stairs and slopes is set up.By introducing the multi-objective optimization method,motion of climbing stairs and slope are realized,which can meet the multi-objective demand.Fourthly,in order to accomplish a complex task,whole body motion planning is necessary.To aim at this problem,a new method of staged whole-body motion planning based on motion capture and resolved momentum control is proposed.In the offline stage,the B spline is used to fit the joint angle and the trajectory is obtained by optimization.In the online stage,the motion control of humanoid robot is realized by combining Model Predictive Control with Resolved Momentum Control.Experimental results show that this method can effectively realize the whole body motion planning for a humanoid robot,and the robot can keep stability during the whole body movement.