Similar articles

Abstract: The pressurized reservoir is a closed hydraulic tank which plays a significant role in enhancing the capabilities of hydraulic driven robotics. The spring pressurized reservoir adopted in this paper requires comprehensive performance, such as weight, size, fluid volume, and pressure, which is hard to balance. A novel interactive multi-objective optimization approach, the feasible space tightening method, is proposed, which is efficient in solving complicated engineering design problems where multiple objectives are determined by multiple design variables. This method provides sufficient information to the designer by visualizing the performance trends within the feasible space as well as its relationship with the design variables. A step towards the final solution could be made by raising the threshold on performance indicators interactively, so that the feasible space is reduced and the remaining solutions are more preferred by the designer. With the help of this new method, the preferred solution of a spring pressurized reservoir is found. Practicability and efficiency are demonstrated in the optimal design process, where the solution is determined within four rounds of interaction between the designer and the optimization program. Tests on the designed prototype show good results.

The paper is interesting, properly written and useful. Pressurized reservoir will play an important role in enhancing the capabilities of hydraulic driven robotics. The purpose of this paper is to evaluate a novel interactive multi-objective optimization approach named feasible space tightening method, which is efficient in solving complicated engineering design problems where multiple objectives are determined by multiple design variables.

液压机器人增压油箱的多目标优化方法研究

[1]Amundson, K., Raade, J., Harding, N., et al., 2006. Development of hybrid hydraulic–electric power units for field and service robots. Advanced Robotics, 20(9):1015-1034.

[2]Blasco, X., Herrero, J., Sanchis, J., et al., 2008. A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20):3908-3924.

[3]Branke, J., Greco, S., Slowinski, R., et al., 2015. Learning value functions in interactive evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation, 19(1):88-102.

[4]Branke, J., Corrente, S., Greco, S., et al., 2016. Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. European Journal of Operational Research, 250(3):884-901.

[5]Chaudhuri, S., Deb, K., 2010. An interactive evolutionary multi-objective optimization and decision making procedure. Applied Soft Computing, 10(2):496-511.

[6]Coello, C.A.C., 2000. Handling preferences in evolutionary multiobjective optimization: a survey. Proceedings of the Congress on Evolutionary Computation, La Jolla, CA, USA, p.30-37.

[7]Deb, K., Pratap, A., Agarwal, S., et al., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182-197.

[8]Jacobsen, S., 2007. On the development of XOS, a powerful exoskeletal robot. IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA.

[9]Klamroth, K., Miettinen, K., 2008. Integrating approximation and interactive decision making in multicriteria optimization. Operations Research, 56(1):222-234.

[10]Kollat, J.B., Reed, P., 2007. A framework for visually interactive decision-making and design using evolutionary multi-objective optimization (VIDEO). Environmental Modelling & Software, 22(12):1691-1704.

[11]Pedro, L.R., Takahashi, R.H., 2013. Decision-maker preference modeling in interactive multiobjective optimization. In: Evolutionary Multi-criterion Optimization, Springer Berlin Heidelberg, p.811-824.

[12]Rachmawati, L., Srinivasan, D., 2006. Preference incorporation in multi-objective evolutionary algorithms: a survey. IEEE Congress on Evolutionary Computation, Vancouver, BC, USA, p.962-968.

[13]Raibert, M., Blankespoor, K., Nelson, G., et al., 2008. Bigdog, the rough-terrain quadruped robot. Proceedings of the 17th World Congress, COEX, South Korea, p.10822-10825.

[14]Semini, C., Tsagarakis, N.G., Guglielmino, E., et al., 2011. Design of HyQ–a hydraulically and electrically actuated quadruped robot. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 225(6):831-849.

[15]Shigley, J.E., Mischke, C.R., Budynas, R.G., et al., 1989. Mechanical Engineering Design. McGraw-Hill, New York, USA.

[16]Sinha, A., Korhonen, P., Wallenius, J., et al., 2014. An interactive evolutionary multi-objective optimization algorithm with a limited number of decision maker calls. European Journal of Operational Research, 233(3):674-688.

[17]Totten, G.E., Bishop, R., 1999. The Hydraulic Pump Inlet Condition: Impact on Hydraulic Pump Cavitation Potential. Technical Paper No. 1999-01-1877.

[18]Vacca, A., Klop, R., Ivantysynova, M., 2010. A numerical approach for the evaluation of the effects of air release and vapour cavitation on effective flow rate of axial piston machines. International Journal of Fluid Power, 11(1):33-45.

[19]Yang, H.Y., Pan, M., 2015. Engineering research in fluid power: a review. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 16(6):427-442.

[20]Yang, H.Y., Feng, B., Gong, G.F., 2011. Measurement of effective fluid bulk modulus in hydraulic system. Journal of Dynamic Systems, Measurement, and Control, 133(6):061021.

[21]Zitzler, E., Thiele, L., 1998. Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Parallel Problem Solving from Nature—PPSN V. Springer Berlin Heidelberg, p.292-301.

[22]Zoss, A.B., Kazerooni, H., Chu, A., 2006. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX). IEEE/ASME Transactions on Mechatronics, 11(2):128-138.