Autonomous Navigation Research And Implemetation Of Mobile Home Service Robot

With the development of the robot technology,more and more researchers focus on the Simultaneous Localization and Mapping(SLAM)which is one of the most important points of service robot.At present,the researchers in this field mainly focuses on feature extraction and feature matching,closed-loop detection and optimization.Recently,Rao-Blackwellized particle filters(RBPF)have been introduced as an effective means to solve the simultaneous localization and mapping problem.This approach uses a particle filter in which each particle carries an individual map of the environment.Accordingly,a key question is how to reduce the number of particles.For local path planning,The classic "elastic band" deforms a path generated by a global planner with respect to the shortest path length while avoiding contact with obstacles.It does not take any dynamic constraints of the underlying robot into account directly.Therefore,this trajectory is not optimal.On the basis of previous RBPF algorithm,an approach is proposed to compute an accurate proposal distribution,taking into account not only the movement of the robot,but also the most recent observation.This drastically decreases the uncertainty about the robot's pose in the prediction step of the filter.Furthermore,an approach is given to selectively carry out resampling operations,which seriously reduces the problem of particle depletion.For the problem of EB,“Timed Elastic Band”(TEB)approach is given.The TEB approach optimizes robot trajectories by subsequent modification of an initial trajectory generated by a global planner.The objectives considered in the trajectory optimization include but are not limited to the overall path length,trajectory execution time,separation from obstacles,passing through intermediate way points and compliance with the robots dynamic,kinematic and geometric constraints.TEB explicitly consider spatial and temporal aspects of the motion in terms of dynamic constraints such as limited robot velocities and accelerations.The trajectory planning operates in real time such that TEB cope with dynamic obstacles and motion constraints.The TEB problem is formulated as a scalarized multi-objective optimization problem.Most objectives are local and relate to only a small subset of parameters as they only depend on a few consecutive robot states.This local structure results in a sparse system matrix,which allows the utilization of fast and efficient optimization techniques such as the open-source framework G2 O for solving TEB problems.The G2 O sparse system solvers have been successfully applied to VSLAM problems.This contribution describes the application and adaptation of the G2 Oframework in the context of trajectory modification with the TEB.For autonomy of robot,a new approach is introduced for exploration based on the concept of frontiers,regions on the boundary between open space and unexplored space.By moving to new frontiers,a mobile robot can extend its map into new territory until the entire environment has been explored.Results from simulations and experiments with a real robot illustrate the advantage of improved RBPF SLAM over previous approaches,indicate that the approach is robust and computationally efficient to generate optimal robot trajectories in real time and prove ability of the Frontier-Based approach to explore both large open spaces and narrow cluttered spaces,with walls and obstacles in arbitrary orientations.