A novel approach was presented to solve the navigation problem of autonomous mobile robots in unknown environments with dense obstacles based on a univector field method. In an obstacle-free environment, a robot is en...A novel approach was presented to solve the navigation problem of autonomous mobile robots in unknown environments with dense obstacles based on a univector field method. In an obstacle-free environment, a robot is ensured to reach the goal position with the desired posture by following the univector field. Contrariwise, the univector field cannot guarantee that the robot will avoid obstacles in environments. In order to create an intelligent mobile robot being able to perform the obstacle avoidance task while following the univector field, Dyna-Q algorithm is developed to train the robot in learning moving directions to attain a collision-free path for its navigation. Simulations on the computer as well as experiments on the real world prove that the proposed algorithm is efficient for training the robot in reaching the goal position with the desired final orientation.展开更多
In order to overcome the inherent oscillation problem of potential field methods(PFMs) for autonomous mobile robots in the presence of obstacles and in narrow passages,an enhanced potential field method that integrate...In order to overcome the inherent oscillation problem of potential field methods(PFMs) for autonomous mobile robots in the presence of obstacles and in narrow passages,an enhanced potential field method that integrates Levenberg-Marquardt(L-M) algorithm and k-trajectory algorithm into the basic PFMs is proposed and simulated.At first,the mobile robot navigation function based on the basic PFMs is established by choosing Gaussian model.Then,the oscillation problem of the navigation function is investigated when a mobile robot nears obstacles and passes through a long and narrow passage,which can cause large computation cost and system instability.At last,the L-M algorithm is adopted to modify the search direction of the navigation function for alleviating the oscillation,while the k-trajectory algorithm is applied to further smooth trajectories.By a series of comparative experiments,the use of the L-M algorithm and k-trajectory algorithm can greatly improve the system performance with the advantages of reducing task completion time and achieving smooth trajectories.展开更多
Mobile robot navigation in unknown environment is an advanced research hotspot.Simultaneous localization and mapping(SLAM)is the key requirement for mobile robot to accomplish navigation.Recently,many researchers stud...Mobile robot navigation in unknown environment is an advanced research hotspot.Simultaneous localization and mapping(SLAM)is the key requirement for mobile robot to accomplish navigation.Recently,many researchers study SLAM by using laser scanners,sonar,camera,etc.This paper proposes a method that consists of a Kinect sensor along with a normal laptop to control a small mobile robot for collecting information and building a global map of an unknown environment on a remote workstation.The information(depth data)is communicated wirelessly.Gmapping(a highly efficient Rao-Blackwellized particle filer to learn grid maps from laser range data)parameters have been optimized to improve the accuracy of the map generation and the laser scan.Experiment is performed on Turtlebot to verify the effectiveness of the proposed method.展开更多
基金Project(2010-0012609) supported by the Basic Science Research Program,Korea
文摘A novel approach was presented to solve the navigation problem of autonomous mobile robots in unknown environments with dense obstacles based on a univector field method. In an obstacle-free environment, a robot is ensured to reach the goal position with the desired posture by following the univector field. Contrariwise, the univector field cannot guarantee that the robot will avoid obstacles in environments. In order to create an intelligent mobile robot being able to perform the obstacle avoidance task while following the univector field, Dyna-Q algorithm is developed to train the robot in learning moving directions to attain a collision-free path for its navigation. Simulations on the computer as well as experiments on the real world prove that the proposed algorithm is efficient for training the robot in reaching the goal position with the desired final orientation.
基金Supported by the National Key Basic Research Program of China(973 Project)(No.2013CB035503)
文摘In order to overcome the inherent oscillation problem of potential field methods(PFMs) for autonomous mobile robots in the presence of obstacles and in narrow passages,an enhanced potential field method that integrates Levenberg-Marquardt(L-M) algorithm and k-trajectory algorithm into the basic PFMs is proposed and simulated.At first,the mobile robot navigation function based on the basic PFMs is established by choosing Gaussian model.Then,the oscillation problem of the navigation function is investigated when a mobile robot nears obstacles and passes through a long and narrow passage,which can cause large computation cost and system instability.At last,the L-M algorithm is adopted to modify the search direction of the navigation function for alleviating the oscillation,while the k-trajectory algorithm is applied to further smooth trajectories.By a series of comparative experiments,the use of the L-M algorithm and k-trajectory algorithm can greatly improve the system performance with the advantages of reducing task completion time and achieving smooth trajectories.
基金National Natural Science Foundation of China(Nos.51475328,61372143,61671321)
文摘Mobile robot navigation in unknown environment is an advanced research hotspot.Simultaneous localization and mapping(SLAM)is the key requirement for mobile robot to accomplish navigation.Recently,many researchers study SLAM by using laser scanners,sonar,camera,etc.This paper proposes a method that consists of a Kinect sensor along with a normal laptop to control a small mobile robot for collecting information and building a global map of an unknown environment on a remote workstation.The information(depth data)is communicated wirelessly.Gmapping(a highly efficient Rao-Blackwellized particle filer to learn grid maps from laser range data)parameters have been optimized to improve the accuracy of the map generation and the laser scan.Experiment is performed on Turtlebot to verify the effectiveness of the proposed method.
