Changes of Locomotion Speed Affect Distance Estimations in Virtual Reality

Modern Virtual Reality (VR) applications often use artificial locomotion to allow users to travel distances within VR space that exceed the available space used to transfer real-world and real-time motion into the virtual environment. The locomotion speed is usually not fixed and can be selected dynamically by the user. Due to motion adaptation effects, variations of locomotion speed could affect how distances in VR are perceived. In the context of cartographic VR applications aimed to experience and communicate spatial information, such effects on distance perception could be problematic, because they might lead to distortions in cognitive representations of space acquired via interaction with VR environments. By conducting a VR-based distance estimation study, we demonstrate how changes of artificial locomotion speed affect distance estimations in VR. Increasing locomotion speeds after letting users adapt to a lower locomotion speed led to lower distance estimations and decreasing locomotion speeds led to higher distance estimations. These findings should sensitize VR developers to consider the choice of applied locomotion techniques when a developed VR application is supposed to communicate distance information or to support the acquisition of a cognitive representation of geographic space.

The performance of a flapping foil for a self-propelled fishlike body

Several fish species propel by oscillating the tail, while the remaining part of the body essentially contributes to the overall drag. Since in this case thrust and drag are in a way separable, most attention was focused on the study of propulsive efficiency for flapping foils under a prescribed stream. We claim here that the swimming performance should be evaluated, as for undulating fish whose drag and thrust are severely entangled, by turning to self-propelled locomotion to find the proper speed and the cost of transport for a given fishlike body. As a major finding, the minimum value of this quantity corresponds to a locomotion speed in a range markedly different from the one associated with the optimal efficiency of the propulsor. A large value of the feathering parameter characterizes the minimum cost of transport while the optimal efficiency is related to a large effective angle of attack. We adopt here a simple two-dimensional model for both inviscid and viscous flows to proof the above statements in the case of self-propelled axial swimming. We believe that such an easy approach gives a way for a direct extension to fully free swimming and to real-life configurations.

Multi-Phase Joint-Angle Trajectory Generation Inspired by Dog Motion for Control of Quadruped Robot

Quadruped robots are receiving great attention as a new means of transportation for various purposes, such as military, welfare, and rehabilitation systems. The use of four legs enables a robustly stable gait; compared to the humanoid robots, the quadruped robots are particularly advantageous in improving the locomotion speed, the maximum payload, and the robustness toward disturbances. However, the more demanding conditions robots are exposed to, the more challenging the trajectory generation of robotic legs becomes. Although various trajectory generation methods (e.x., central pattern generator, finite states machine) have been developed for this purpose, these methods have limited degrees of freedom with respect to the gait transition. The conventional methods do not consider the transition of the gait phase (i.e., walk, amble, trot, canter, and gallop) or use a pre-determined fixed gait phase. Additionally, some research teams have developed locomotion algorithms that take into account the transition of the gait phase. Still, the transition of the gait phase is limited (mostly from walking to trot), and the transition according to gait speed is not considered. In this paper, a multi-phase joint-angle trajectory generation algorithm is proposed for the quadruped robot. The joint-angles of an animal are expressed as a cyclic basis function, and an input to the basis function is manipulated to realize the joint-angle trajectories in multiple gait phases as desired. To control the desired input of a cyclic basis function, a synchronization function is formulated, by which the motions of legs are designed to have proper ground contact sequences with each other. In the gait of animals, each gait phase is optimal for a certain speed, and thus transition of the gait phases is necessary for effective increase or decrease in the locomotion speed. The classification of the gait phases, however, is discrete, and thus the resultant joint-angle trajectories may be discontinuous due to the transition. For the smooth and continuous transition of gait phases, fuzzy logic is utilized in the proposed algorithm. The proposed methods are verified by simulation studies.

Inhibitory hippocampo–septal projection controls locomotion and exploratory behavior

Although the hippocampus is generally considered a cognitive center for spatial representation, learning and memory, increasing evidence supports its roles in regulation of locomotion. However, the neuronal mechanisms of hippocampal regulation of locomotion and exploratory behavior remain unclear. Here we found that the inhibitory hippocampo-septal projection bi–directionally controls locomotion speed of mice. Pharmacogenetic activation of these septum–projecting interneurons decreased locomotion and exploratory behavior. Similarly, activation of the hippocampus–originated inhibitory terminal in the medial septum reduced locomotion. On the other hand, inhibition of the hippocampus–originated inhibitory terminal increased locomotion. The locomotion-regulative roles were specific to the septal projecting interneurons as activation of hippocampal interneurons projecting to the retrosplenial cortex did not change animal locomotion. Therefore, this study reveals a specific long-range inhibitory output from the hippocampus in the regulation of animal locomotion.

Multidimensional encoding of movement and contextual variables by rat globus pallidus neurons during a novel environment exposure task

The basal ganglia (BG) play a critical role in a variety of functions that are essential for animal survival. Information from different cortical areas propagates through the BG in anatomically segregated circuits along the parallel direct and indirect pathways. We examined how the globus pallidus (GP), a central nucleus within the indirect pathway, encodes input from the motor and cognitive domains. We chronically recorded and analyzed neuronal activity in the GP of rats engaged in a novel environment exposure task. GP neurons displayed multidimensional responses to movement and contextual information. A model predicting single unit activity required many task-related variables, thus confirming the multidimensionality of GP neurons. In addition, populations of GP neurons, but not single units, reliably encoded the animals' locomotion speed and the environmental novelty. We posit that the GP independently processes information from different domains, effectively compresses it and collectively conveys it to successive nuclei.

The tectonigral pathway regulates appetitive locomotion in predatory hunting in mice

Appetitive locomotion is essential for animals to approach rewards, such as food and prey. The neuronal circuitry controlling appetitive locomotion is unclear. In a goal-directed behavior—predatory hunting, we show an excitatory brain circuit from the superior colliculus (SC) to the substantia nigra pars compacta (SNc) to enhance appetitive locomotion in mice. This tectonigral pathway transmits locomotion-speed signals to dopamine neurons and triggers dopamine release in the dorsal striatum. Synaptic inactivation of this pathway impairs appetitive locomotion but not defensive locomotion. Conversely, activation of this pathway increases the speed and frequency of approach during predatory hunting, an effect that depends on the activities of SNc dopamine neurons. Together, these data reveal that the SC regulates locomotion-speed signals to SNc dopamine neurons to enhance appetitive locomotion in mice.

Kinetic and Kinematic Analysis of Locomotion Speed of Higher Secondary Boys

The purpose was to analyze selected components of locomotion speed. Thirty school boys of age ranging from 17 to 19 year were selected as subject. Selected anthropometric and mechanical parameters were body weight, body height, leg length, maximum locomotion speed, leg power, stride length, stride frequency, body inclination, angle of leg placement in braking phase, push-off angle, horizontal projection of CG in braking phase, horizontal projection of CG in propulsion phase, horizontal velocity of CG in braking phase, horizontal velocity of CG in propulsion phase, velocity of swing leg in braking phase, velocity of swing leg in propulsion phase, angular velocity of thigh in propulsion phase, contact phase, flight phase, braking phase, and propulsion phase. The maximum locomotion speed was determined by a field test. Running action was filmed by a digital video camera with 120 fps for the distance between 40 to 50 m of the 100 m race. The anthropometric parameters were measured using standard procedure. The selected mechanical parameters were analyzed by motion analysis software. Results showed that weight, height, leg length, stride length, stride frequency had higher positive correlation with maximum locomotion velocity, whereas, contact phase, flight phase and propulsion phase had higher negative correlation with maximum locomotion velocity.

Locomotion speed capability analysis of six-legged robots: Optimization and application

Locomotion speed is a key performance index of legged robots. However, methods to analyze and improve the locomotion speed capability are seldom developed, especially for six-legged robots. This paper develops a method to analyze and improve the omnidirectional walking speed and the turning speed of six-legged robots. The models of the inverse kinematics and the influence coefficients are built. Making use of the only-position-related property of the influence coefficients, a general optimization model of the locomotion trajectory is established. A two-step optimization method is introduced to solve the optimization problem. Based on the optimization, a comprehensive speed capability analysis is conducted on both omnidirectional walking and turning of the six-parallel-legged robot. The results clearly show the relationships among the speed capability, the walking direction and the duty cycle. The two-step optimization method improves the speed capability by 12.4%–13.2% for turning and 18.5%–20.5% for omnidirectional walking. Finally, the costs of the speed improvement are analyzed, including the stability, the energy consumption and the calculation time.

Vibration-Based Locomotion of an Amphibious Robot

In this research, an innovative robot is presented that can move both on land and water thanks to a vibration-based locomotion mechanism. The robot consists of a U-shaped beam made of spring steel, two low-density feet that allow it to stand on the water surface without sinking, and a micro-DC motor with eccentric mass, which excites vibrations. The robot exhibits stable terrestrial and aquatic locomotion based on the synchronization between body vibrations and the centrifugal force due to the eccentric mass. On the one hand, in aquatic locomotion, the robot advances thanks to floating oscillations and the asymmetric shape of the floating feet. On the other hand, the terrestrial locomotion, which has already been demonstrated for a similar robot, exploits the modes of vibration of the elastic beam. In this study, the effect of different excitation frequencies on the locomotion speed in water is examined by means of experimental tests and a numerical model. A good agreement between experimental and numerical results is found. The maximum locomotion speed takes place when the floating modes of vibration are excited.

Electronics-free pneumatic circuits for controlling soft-legged robots

Pneumatically actuated soft robots have recently shown promise for their ability to adapt to their environment. Previously, these robots have been controlled with electromechanical components, such as valves and pumps, that are typically bulky and expensive. Here, we present an approach for controlling the gaits of soft-legged robots using simple pneumatic circuits without any electronic components. This approach produces locomotive gaits using ring oscillators composed of soft valves that generate oscillating signals analogous to biological central pattern generator neural circuits, which are acted upon by pneumatic logic components in response to sensor inputs. Our robot requires only a constant source of pressurized air to power both control and actuation systems. We demonstrate this approach by designing pneumatic control circuits to generate walking gaits for a soft-legged quadruped with three degrees of freedom per leg and to switch between gaits to control the direction of locomotion. In experiments, we controlled a basic walking gait using only three pneumatic memory elements (valves). With two oscillator circuits (seven valves), we were able to improve locomotion speed by 270%. Furthermore, with a pneumatic memory element we designed to mimic a double-pole double-throw switch, we demonstrated a control circuit that allowed the robot to select between gaits for omnidirectional locomotion and to respond to sensor input. This work represents a step toward fully autonomous, electronics-free walking robots for applications including low-cost robotics for entertainment and systems for operation in environments where electronics may not be suitable.