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This paper proposes a method to produce the stable walking of humanoid robots by incorporating the vertical center of mass (COM) and foot motions, which are generated by the evolutionary optimized central pattern generator (CPG), into the modifiable walking pattern generator (MWPG). The MWPG extends the conventional 3-D linear inverted pendulum model (3-D LIPM) by allowing a zero moment point (ZMP) variation. The disturbance caused by the vertical COM motion is compensated in real time by the sensory feedback in the CPG. In this paper, the vertical foot trajectory of the swinging leg, as well as the vertical COM trajectory of the 3-D LIPM, are generated by the CPG for the effective compensation of the disturbance. Consequently, using the proposed method, the humanoid robot is able to walk with a vertical COM and the foot motions generated by the CPG, while modifying its walking patterns by using the MWPG in real time. The CPG with the sensory feedback is optimized to obtain the desired output signals. The optimization of the CPG is formulated as a constrained optimization problem with equality constraints and is solved by two-phase evolutionary programming (TPEP).The validity of the proposed method is verified through walking experiments for the small-sized humanoid robot, HanSaRam-IX (HSR-IX).

This paper proposes a novel online object-packing system which can measure the dimensions of every incoming object and calculate its desired position in a given container. Existing object-packing systems have the limitations of requiring the exact information of objects in advance or assuming them as boxes. Thus, this paper is mainly focused on the following two points: (1) Real-time calculation of the dimensions and orientation of an object; (2) Online optimization of the object’s position in a container. The dimensions and orientation of the object are obtained using an RGB-D sensor when the object is picked by a manipulator and moved over a certain position. The optimal position of the object is calculated by recognizing the container’s available space using another RGB-D sensor and minimizing the cost function that is formulated by the available space information and the optimization criteria inspired by the way people place things. The experimental results show that the proposed system successfully places the incoming various shaped objects in their proper positions.

Keywords:Multiobjective evolutionary algorithm, quantum-inspired evolutionary algorithm, preference-based evolutionary algorithmAbstract: This paper proposes dual multiobjective quantum-inspired evolutionary algorithm (DMQEA) with the dualstage of dominance check by introducing secondary objectives in addition to primary objectives. The secondary objectives are to maximize global evaluation values and crowding distances of the solutions in the external global population obtained for the primary objectives and the previous archive obtained from the secondary objectives-based nondominated sorting. By employing the secondary objectives for sorting the solutions in each generation, DMQEA can induce the balanced exploration of the solutions in terms of user's preference and diversity to generate preferable and diverse nondominated solutions in the archive. To demonstrate the effectiveness of the proposed DMQEA, empirical comparisons with MQEA, MQEA-PS, and NSGA-II are carried out for benchmark functions.

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