Abstract
There is huge diversity among navigation and path-planning problems in the real world because of the enormous number and great variety of assumptions about the environments, constraints, and tasks imposed on a robot. To deal with this diversity, we propose a new solution to the path-planning and navigation of a mobile robot. In our approach, we formulated the following two problems at each time-step as discrete optimization problems: (1) estimation of a robot's location, and (2) action decision. For the first problem, we minimize an objective function that includes a data term, a constraint term, and a prediction term. This approach is an approximation of Markov localization. For the second problem, we define and minimize another objective function that includes a goal term, a smoothness term, and a collision term. Simulation results show the effectiveness of our approach.
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