Investigating users' plausible preferences for body-centric locomotion can help researchers better understand the impact of different factors on this locomotion method and optimize the locomotion configuration for providing a plausible locomotion experience. In this paper, we propose to evaluate users' plausible preferences for body-centric locomotion using a reinforcement learning method. This method can intelligently infer users' plausible preferences for different factor levels involved in the virtual locomotion by proposing possible modifications to the factor levels and asking users to accept or reject the modifications after experiencing the locomotion. We conducted a within-subject experiment to examine the impact of different factors (i.e., body parts used for virtual locomotion, the point of view, auditory feedback, the transfer function, and the coefficients of the transfer function) on users' plausible preferences for body-centric locomotion in sitting and standing postures. The results mainly indicated that (1) users preferred using arm swinging in standing posture, whereas they preferred using head tilting in sitting posture; (2) The point of view was identified as the most important factor in standing posture, while it was less important in sitting posture; (3) Participants showed consistent plausible preferences for auditory feedback, transfer function, and coefficient of the transfer function in both standing and sitting postures. Our research findings can guide the design of VR applications to enhance a plausible walking experience in different postures.

Body-centric locomotion in Virtual Reality (VR) involves multiple factors, including the point of view, avatar representations, tracked body parts for locomotion control and transfer functions that map body movement to the displacement of the virtual viewpoint. Understanding the role of these factors in evoking a plausible walking experience using within- or between-subject experimental designs based on questionnaires and/or objective measurements can be time-consuming and challenging due to the interrelated effects of these factors. This study employed the subjective matching method to evaluate the sense of plausible walking experience during body-centric locomotion in VR. Five relevant factors that may affect locomotion experience were identified by analyzing existing studies, i.e., point of view, the avatar appearance, body parts for locomotion control, transfer functions and the coefficients of transfer functions. A virtual locomotion experiment with these five factors based on subjective matching was conducted. Results showed that participants regarded the point of view as the most critical factor for walking experience enhancement, followed by body parts, transfer functions, the coefficients of transfer functions and finally the avatar appearance. Additionally, participants’ preferences for different body parts and the coefficients of transfer functions affected the choice of transfer functions. These results could serve as the guidelines for virtual locomotion experience design that involves combinations of multiple factors and can help achieve a plausible walking experience in VR.

This paper explored a way to design mechanical structure and motion pattern of a quadrupedal robot to realize pitching motion by mimicking a cat. Four joints configuration styles were obtained by simplifying quadrupedal mammal's body. Referring to the leg motion of a cat, joint motion trajectories were designed and a motion mapping relationship between knee and hip was formulated for the quadrupedal robot. Proved by the experiments with a physical robot, these designs are effective and the quadrupedal robot can walk quickly and smoothly just as a cat does. Otherwise, we find robotic mechanical structure in terms of joint-configuration style, layout of COG, etc. has influences on the performance of the robot. That results in slippage or not-clearance-from-the-ground of feet in motion.

For walking machine, its motion performance can be enhanced evidently and effectively by biocybernetics-based approach. Since animals commonly act by rhythmic movement, and the rhythmic movement signal is generated by Central Pattern Generator (CPG) in its body. In this paper, we designed a bionic quadruped system, including its machine structure and control system, based on the studying of the CPG mechanism, the structure of rhythmic movement control network and the CPG differential equation model. The quadruped we completed have manifold bionic functions: walking on flat ground with four typical gaits (walk, pace, trot, gallop), changing its gait smoothly and leg configuration styles; and some advanced functions such as climbing a slope and overcoming an obstacle, etc. is in the process of experiments.

Legged robot generally moves according to the preplanned gait, which cannot coordinate the motions among the legs with environment. Petri-net is a graphic assembling net model that can be applied to describe and control the dynamic behaviors in multi-agent system. Considering the motion characters of legged robot, this paper proposed a single-leg Petri-net control model for legged robot, and defined the corresponding model parameters. As an example, five such models were assembled to construct the coordination motion control model for the five-legged robot we developed. On this base, the reachable marking graph including all moving states of the five-legged robot was calculated and expressed. Applying the Petri-net model, a new free gait was presented. At last, the motion of five-legged robot crossing grooves in discrete area was simulated. The results show that the five-legged robot can adapt to the environment automatically with the Petri-net control model in this paper.