International Journal of Humanoid Robotics

This paper presents efficient geometric algorithms to analytically evaluate the first- and second-order information of humanoid dynamics within a trajectory optimization scheme. In particular, the exact Hessians of the robot’s center of mass and the centroidal momentum are provided to the optimization solver for synthesizing dynamically feasible humanoid motions. The transcription method applies direct collocation to parametrize the humanoid’s configuration space with B-Spline curves. The proposed algorithms avoid the computation of redundant information by exploiting the commutativity of the Kronecker product and the symmetry in second-order derivatives. We validate the effectiveness of the optimization strategy with the humanoid robot models Nao and Atlas. (The C++ code can be found in github.com/AlvaroPaz/BSplinesTrajOpt.)

The gait cycle of 25-degree of freedom (DOF) humanoid robot, namely NAO robot, consists of single support phase (SSP) and double support phase (DSP). Both dynamic and stability analyses are carried out for this robot to determine its power consumption and dynamic stability margin, respectively. Constrained single-objective optimization problems are formulated for the SSP and DSP separately and solved using particle swarm optimization (PSO) and genetic algorithms (GA). A performance index, other than the fitness function, consisting of constraint values and maximum swing height, is also considered to compare PSO and GA-obtained optimal solutions. PSO is able to find the trajectories that offer higher swing height for nearly similar power consumption during SSP. A performance assessment of each algorithm based on the best fitness values in each generation across several runs is also carried out. These values are compared using the Wilcoxon rank-sum test, and PSO is found to be statistically better than GA. The optimal solutions from the simulations are tested using the Webots simulator to validate their efficacy on stability. Moreover, an investigation of the influence of gait parameters on power consumption during SSP and DSP reveals that the humanoid robot with a higher hip height, lower swing height, and slow pace consumes less power. The methodology developed in this is generic and can be easily extended to other robots.

Despite the social robotics’ potential to revolutionize human life, the field of social robotics has been less appealing and less popular than other robotics fields in both industrial and research domains. Therefore, many students are unfamiliar with this field. To expedite the development of the social robotics sector, numerous efforts must be made, including introducing students to the topic. In this study, we developed teaching materials and a procedure for introducing university students to the topic of social robotics. A robot demonstration was also utilized to provide participants with a more engaging experience and a deeper knowledge of the importance of social robotics. The ultimate objective of this paper is to provide a reference for lecturers and instructors in introducing social robots to students in the hope that they will become interested in the field, whether for the purposes of research or simply to learn more about this topic. We evaluated the proposed teaching materials and procedures for a small class size based on behavioral and subjective measurements. We also compared the outcome of presentations with and without a robot demonstration. In addition, we performed semi-structured interviews with 10 random participants who wanted to expand our qualitative analysis. Both presentations, with and without robot demonstration, successfully delivered the material. The experimental findings indicated that the robot demonstration had a significant impact on students’ satisfaction with the presentation and their desire to participate in similar presentations in the future.