Robust Vibration Control Based on Rigid-Body State Observer for Modular Joints

The vibration caused by resonance modes frequently occurs during acceleration and deceleration of the modular joint integrated with flexible harmonic drive. The conventional equivalent rigid-body velocity method with observer can suppress the residual vibration induced by resonant frequency but has poor robustness to model uncertainties and external disturbances. Moreover, it cannot eliminate the torque ripple caused by the harmonic drive during low-speed uniform motion, reducing the velocity tracking accuracy. Hence, a velocity controller with a rigid-body state observer and an adjustable damper is designed to improve the robust performance and velocity tracking accuracy. The designed rigid-body state observer allows a higher gain so that the bandwidth of the observer can increase, and the equivalent rigid-body velocity can be acquired more accurately. Notably, the high gain observer reduces the sensitivity to model uncertainties and exotic disturbances, especially near the resonant frequency. In addition, the observer combined with an adjustable damper can suppress the residual vibration and torque ripple simultaneously. The proposed method is compared experimentally with a PI method and two other rigid-body velocity methods, such as the conventional equivalent rigid-body observer method and the self-resonance cancellation method, to verify its advantages.

Trait-Based Variation in the Foraging Performance of Individuals

Although average, species-level interaction strength plays a key role in driving population dynamics and community structure, predator-prey interactions occur among individuals. As a result, individual variation in foraging rates may play an important role in determining the effects of predator-prey interactions on communities. Such variation in foraging rates stems from individual variation in traits that influence the mechanistic components of the functional response, such as movements that determine encounters and behaviors such as decisions to attack. However, we still have little information about individual-level variation in functional responses or the traits that give rise to such variation. Here we combine a standard functional response experiment with wolf spiders foraging on fruit flies with a novel analysis to connect individual morphology, physiology, and movement to individual foraging performance. We found substantial variation in traits between males and females, but these were not clearly linked to the differences in the functional response between males and females. Contrary to expectations, we found no effect of body velocity, leg length, energetic state, or metabolic rate on foraging performance. Instead, we found that body mass interacted with body rotations (clockwise turns), such that larger spiders showed higher foraging performance when they turned more but the reverse was true for smaller spiders. Our results highlight the need to understand the apparent complexity of the links between the traits of individuals and the functional response.

A Vehicle-Model-Aided Navigation Reconstruction Method for a Multicopter during a GPS Outage

The integrated navigation of inertial navigation systems (INS) and the Global Positioning System (GPS) is essential for small unmanned aerial vehicles (UAVs) such as multicopters, providing steady and accurate position, velocity, and attitude information. Nevertheless, decreasing navigation accuracy is a serious threat to flight safety due to the long-term drift error of INS in the absence of GPS measurements. To bridge the GPS outage for multicopters, this paper proposes a novel navigation reconstruction method for small multicopters, which combines the vehicle dynamic model and micro-electro-mechanical system (MEMS) sensors. Firstly, an induced drag model is introduced into the dynamic model of the vehicle, and an efficient online parameter identification method is designed to estimate the model parameters quickly. Secondly, the body velocity can be calculated from the vehicle model and accelerometer measurement. In addition, the nongravitational acceleration estimated from body velocity and radar height are utilized to yield a more accurate attitude estimate. Fusing the information of the attitude, body velocity, magnetic heading, and radar height, a navigation system based on an error-state Kalman filter is reconstructed. Then, an adaptive measurement covariance algorithm based on a fuzzy logic system is designed to reduce the weight due to the disturbed acceleration. Finally, the hardware-in-loop experiment is carried out to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed navigation reconstruction algorithm aided by the vehicle model can significantly improve navigation accuracy during a GPS outage.

A Robust Method Based on Dual Encoders to Eliminate Velocity Ripple for Modular Drive Joints

Velocity ripple is one of the common problems of modular drive joints, which easily induces vibration and noise and affects motion accuracy. In order to improve the motion control accuracy, a robust method based on dual encoders to eliminate velocity ripple is proposed in this paper. The method contains a velocity ripple elimination controller (VREC), a rigid-body velocity solver (RBVS), and a proportional–integral (PI) controller. Feeding back the VREC output to the PI controller based on the rigid-body velocity obtained from the weighted sum of dual encoders in the RBVS, an equivalent system damping term was added into the system. Therefore, the velocity ripple can be suppressed effectively with the adjustable damping term composed of control parameters. Above all, the proposed method has only one more parameter to further eliminate velocity ripple compared to the pure PI method and, meanwhile, has apparent advantages over the conventional method, such as fewer parameters and full frequency ripple elimination, as well as robustness to input disturbance and modular drive joint load inertia changes. This proposed method’s effectiveness is verified by simulations in MATLAB and experiments in the modular drive joint platform.

Velocimetry Based on Self-Generated Surface Wave Patterns

This paper introduces an image analysis technique applied to an artificially-created disturbance at the free surface of a moving water body as a means of quantifying the average velocity of the water stream for shallow flows. The disturbance was created by a thin object penetrating the free surface with different submerged distances. A V-shaped wake pattern was created by the object of interest through its variation with the water body velocity, the submergence and shape of the piercing body. The angle of the wake pattern decreased with the increase of the velocity for a depth-based Froude number ranging from 0.15 to 0.96. The proof-of-concept experiments presented in this paper, therefore, are usable to quantify the velocity based on the wake angle only in subcritical flow conditions. The results showed the shape of the wake was only slightly influenced by the shape of the object geometry and its submergence. Observations on various types of surface wakes have been documented before, but it is the conversion of these observations into a relatively inexpensive and robust method to estimate the velocity of the moving body that is deemed innovative.

Learning agile and dynamic motor skills for legged robots

Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship and promotes the natural evolution of a control policy. However, so far, reinforcement learning research for legged robots is mainly limited to simulation, and only few and comparably simple examples have been deployed on real systems. The primary reason is that training with real robots, particularly with dynamically balancing systems, is complicated and expensive. In the present work, we introduce a method for training a neural network policy in simulation and transferring it to a state-of-the-art legged system, thereby leveraging fast, automated, and cost-effective data generation schemes. The approach is applied to the ANYmal robot, a sophisticated medium-dog–sized quadrupedal system. Using policies trained in simulation, the quadrupedal machine achieves locomotion skills that go beyond what had been achieved with prior methods: ANYmal is capable of precisely and energy-efficiently following high-level body velocity commands, running faster than before, and recovering from falling even in complex configurations.

Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics

The ability of vertebrates to generate rhythm within their spinal neural networks is essential for walking, running, and other rhythmic behaviors. The central pattern generator (CPG) network responsible for these behaviors is well-characterized with experimental and theoretical studies, and it can be formulated as a nonlinear dynamical system. The underlying mechanism responsible for locomotor behavior can be expressed as the process of leaky integration with resetting states generating appropriate phases for changing body velocity. The low-dimensional input to the CPG model generates the bilateral pattern of swing and stance modulation for each limb and is consistent with the desired limb speed as the input command. To test the minimal configuration of required parameters for this model, we reduced the system of equations representing CPG for a single limb and provided the analytical solution with two complementary methods. The analytical and empirical cycle durations were similar (R2 = 0.99) for the full range of walking speeds. The structure of solution is consistent with the use of limb speed as the input domain for the CPG network. Moreover, the reciprocal interaction between two leaky integration processes representing a CPG for two limbs was sufficient to capture fundamental experimental dynamics associated with the control of heading direction. This analysis provides further support for the embedded velocity or limb speed representation within spinal neural pathways involved in rhythm generation.