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Abstract-In this paper, an Instant Goal approach is proposed for collision-free boundary following of obstacles of arbitrary shape and globally convergent path planning in unknown environments. Firstly, for effective knowledge representation and manipulation, a vector representation is presented, which not only saves much space but also conforms to the physical properties of range sensors. Secondly, the concept of instant goals is introduced enabling the robot to perform boundary following in a "natural" human-like manner, with additional measures taken to ensure that the robot is moving "forward" along the boundary, even if the obstacle is of arbitrary shape and disturbing obstacles are present. Collision checking is performed simultaneously and, when needed, collision avoidance is efficiently incorporated in. Based on the approach of boundary following, a realistic sensorbased path planner with global convergence property is designed for the robot capable of acquiring discrete, and noisy range data. Realistic simulation experiments validate the effectiveness of the proposed approaches.

Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms.

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