Abstract
We applied a brute-force value iteration algorithm to mobile robot navigation. Value iteration is computationally more expensive than search methods used for navigation. However, it can perfectly calculate the expected cost-to-go from any point in a state space. From this cost data, a robot can know not only the optimal behavior at any position and orientation but also the appropriate detour path against suddenly appearing obstacles. This study implemented value iteration and investigated its properties through experiments with simulated and actual robots. Although its computational cost remained high, our implementation could operate a robot in an actual outdoor environment with 3,700 m2 free space. We also verified that our implementation calculates long detour paths toward closures composed of obstacles.
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