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In real applications, mobile robot may be commanded to go to multiple goals to execute special commissions. This study analyzes the particular properties of this multiple goals visiting task and proposes a novel tailored genetic algorithm for optimal path planning for this task. In proposed algorithm, objectives for evaluating the path are energy consumption and idle time that are proposed in our previous work. Under the constraint of energy consumption, it will generate an optimal path that comprises as more goals as possible and as less idle time as possible. In this algorithm, customized chromosome representing a path and genetic operators including Repair, Cut and Deletion are developed and implemented. Afterwards, simulations are carried out to verify the effectiveness and applicability. Finally, analysis of simulation results is conducted and future work is addressed.

This paper presents a new class of flexure hinges, namely, conic-V-shaped flexure hinges (CFHs), which can be used as a generalized model for flexure hinges with profiles such as parabolic-V-shape, elliptical-V-shape, and hyperbolic-V-shape. Compliance and precision equations for the CFHs were derived as a set of nonlinear equations using Castigliano’s second theorem. The parameters of the nonlinear equations inputted to the compliance and precision matrices were based on the generalized equations used for conic curves in polar coordinates. Furthermore, the compliance equations were verified by means of finite element analysis and experiments. The errors in the finite element and experimental results were within 10% and 8% compared to the analytical results, respectively. Finally, the effects of dimensional parameters on the analytical model could be effectively analyzed by numerical simulations and comparisons.

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