Navigation of a wheeled robot in unknown environments is proposed in this paper. The approach may be applied to navigating an autonomous vehicle in unknown environments, such as parking lots. The navigation consists of three parts: obstacle avoidance behavior, target seeking behavior, and a behavior supervisor. The obstacle avoidance behavior is achieved by controlling the robot to move along an obstacle boundary through evolutionary fuzzy control. In the evolutionary fuzzy control approach, a Pareto set of fuzzy controllers (FCs) is found though a multi-objective continuous ant colony optimization algorithm. Target seeking behavior is achieved by controlling the robot through hybrid proportional–integral–derivative (PID) controllers. The behavior supervisor determines the switching between obstacle avoidance and target seeking behaviors, where the dead-cycle problem is considered. Simulations and experiments were performed to verify the effectiveness of the proposed navigation scheme.
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