Collision avoidance by a modified least-mean-square-error classification scheme for indoor autonomous land vehicle navigation

In this article, a new collision-avoidance scheme is proposed for autonomous land vehicle (ALV) navigation in indoor corridors. The goal is to conduct indoor collisionfree navigation of a three-wheel ALV among static obstacles with no a priori position information as well as moving obstacles with unknown trajectories. Based on the predicted positions of obstacles, a local collision-free path is computed by the use of a modified version of the least-mean-square-error (LMSE) classifier in pattern recognition. Wall and obstacle boundaries are sampled as a set of 2D coordinates, which are then viewed as feature points. Different weights are assigned to different feature points according to the distances of the feature points to the ALV location to reflect the locality of path planning. The trajectory of each obstacle is predicted by a real-time LMSE estimation method. And the maneuvering board technique used for nautical navigation is employed to determine the speed of the ALV for each navigation cycle. Smooth collision-free paths found in the simulation results are presented to show the feasibility of the proposed approach.

[1]  Ronald C. Arkin,et al.  Navigational path planning for a vision-based mobile robot , 1989, Robotica.

[2]  Nasser Kehtarnavaz,et al.  A collision-free navigation scheme in the presence of moving obstacles , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Ronald C. Arkin,et al.  Motor Schema — Based Mobile Robot Navigation , 1989, Int. J. Robotics Res..

[4]  Larry S. Davis,et al.  A visual navigation system for autonomous land vehicles , 1987, IEEE J. Robotics Autom..

[5]  Robert F. Cromp,et al.  The design of an autonomous vehicle for the disabled , 1986, IEEE J. Robotics Autom..

[6]  A.C.-C. Meng.  Free space modeling and geometric motion planning under unexpected obstacles , 1988, [1988] Proceedings. The Fourth Conference on Artificial Intelligence Applications.

[7]  Larry S. Davis,et al.  Multiresolution path planning for mobile robots , 1986, IEEE J. Robotics Autom..

[8]  Osamu Takahashi,et al.  Motion planning in a plane using generalized Voronoi diagrams , 1989, IEEE Trans. Robotics Autom..

[9]  Hans P. Moravec,et al.  The Stanford Cart and the CMU Rover , 1983, Proceedings of the IEEE.

[10]  Gordon T. Wilfong.  Motion planning for an autonomous vehicle , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[11]  Rodney A. Brooks,et al.  Natural decomposition of free space for path planning , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[12]  Koren,et al.  Real-Time Obstacle Avoidance for Fast Mobile Robots , 2022 .

[13]  Rodney A. Brooks,et al.  Solving the find-path problem by good representation of free space , 1982, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  S. Zucker,et al.  Toward Efficient Trajectory Planning: The Path-Velocity Decomposition , 1986 .

[15]  Takeo Kanade,et al.  Vision and Navigation for the Carnegie-Mellon Navlab , 1987 .

[16]  Hanan Samet,et al.  A hierarchical strategy for path planning among moving obstacles [mobile robot] , 1989, IEEE Trans. Robotics Autom..

[17]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[18]  Robert Evans,et al.  A Maneuvering-Board Approach to Path Planning with Moving Obstacles , 1989, IJCAI.