A technique is developed for vehicle navigation and control in the presence of obstacles. A potential function was devised that peaks at the surface of obstacles and has its minimum at the proper vehicle destination. This function is computed using a systolic array and is guaranteed not to have local minima. A feedfoward neural network is then used to control the steering of the vehicle using local potential field information. In this case, the vehicle is a trailer truck backing up. Previous work has demonstrated the capability of a neural network to control steering of such a trailer truck backing to a loading platform, but without obstacles. Now, the neural network was able to learn to navigate a trailer truck around obstacles while backing toward its destination. The network is trained in an obstacle free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.
Cascading a Systolic Array and a Feedforward Neural Network for Navigation and Obstacle Avoidance Using Potential Fields
1991
23 pages
Report
Keine Angabe
Englisch
Navigation & Guidance System Components , Computers, Control & Information Theory , Neural nets , Obstacle avoidance , Surface navigation , Systolic arrays , Feedforward control , Algorithms , Computer networks , Potential fields , Hybrid navigation systems , Interpolation , Minima , Network analysis , Relaxation method (Mathematics) , Steering , Trajectory control , Trucks
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