This article presents a visual navigation and landing control paradigm for an unmanned aerial vehicle (UAV) to land on a moving autonomous surface vehicle (ASV). Therein, an adaptive learning navigation rule with a multilayer nested guidance is designed to pinpoint the position of the ASV and to guide and control the UAV to fulfill horizontal tracking and vertical descending in a narrow landing region of the ASV by means of merely relative position feedback. To ensure the feasibility of the proposed control law, asymptotical stability conditions are derived based on Lyapunov stability theory. Landing experimental results are reported for a UAV-ASV system consisting of an M-100 UAV and a self-developed three-meters-long HUSTER-30 ASV on a lake to substantiate the efficacy of the proposed landing control method.

Swing Leg Retraction (SLR) is observed in human walking and running. Previous studies have concluded that SLR improves the stability and robustness of biped walking. But this conclusion was based on analysis of robot models that can only walk at a very small range of step-lengths and slow or fixed speeds. By contrast, humans can walk with a large range of speeds and step-lengths. Moreover, human walking patterns have a special feature that has not been considered in the previous studies on SLR effects: At a given walking speed, v, humans prefer a step-length, s, which satisfies the power law, s-vβ. Therefore, previous studies on SLR can't tell us whether their conclusion will still hold in the full range of human walking patterns (i.e., various walking speeds and step-lengths). This is the question we want to answer in this paper. In this study, using a simple biped model, we studied how the SLR affects the walking stability in the full range of human walking speeds/step-lengths. Preliminary analysis of both models suggests the same conclusion: (1) SLR improves the stability more evidently in human-preferred walking patterns than in other walking patterns. (2) In walking patterns that are very unlike human-preferred ones, the SLR improves the stability very little, or even deteriorates it drastically. Therefore, the new finding of our study is that how the SLR affects the biped walking stability depends on the walking speed and step-length. SLR does not always improve the stability of biped walking.

There is a category of biped robots that are equipped with passive or un-actuated ankles, which we call Passive-Ankle Walkers (PAWs). Lack of actuation at ankles is a disadvantage in the fast walking of PAWs. We started this study with an intuitive hypothesis that rhythmic sway of torso may enable faster walking in PAWs. To test this hypothesis, firstly, we optimized the rhythmic sway of torso of a simulated PAW model for fast walking speed, and analyzed the robustness of the optimal trajectories. Then we implemented the optimal trajectories on a real robot. Both the simulation analysis and the experimental results indicated that optimized torso-swaying can greatly increase the walking speed by 40%. By analyzing the walking patterns of the simulated model and the real robot, we identified the reason for the faster walking with swaying-torso: The rhythmic sway of torso enables the robot to walk with a relatively large step-length while still keeninu a hizh sten-frenuencv.

Like level-ground walking, biological experiments have shown that humans largely maintain compliant leg behavior during slope walking, which greatly reduces the mechanical cost of transportation. Nowadays biped robots are becoming more and more functional on irregular terrains, yet no theoretical model can describe explicitly the cause of the characteristic ground reaction force (GRF) patterns observed in human slope walking. To fill the knowledge gap, this study extended Geyer's template biped level-ground walking model to explaining the slope walking GRF. By comparing the current Geyer's model with the human slope walking data, it was reasoned out that only using radial force from the legs could not account for the shifted anterior-posterior (AP) GRF without breaking the compliant leg behavior in the normal direction. With introducing the leg rotary force, the extended Geyer's model was then able to address the shifted AP GRF effectively. For legged robotics, this study primarily suggests that letting the leg rotary force collaborate with the compliant leg behaviour can improve the agility and energy-efficiency of dynamic walking on irregular terrains.

The class of Pneumatically-driven Lower-pressure Soft Actuators (PLSA) is a popular research topic as it can be potentially used in the surgical robotic applications. One fundamental problem lying in the PLSA research is the lack of a generally validated model for the complex nonlinear dynamic behaviours. In this paper, a new coefficient-adaptive orthonormal basis function model structure is specifically developed for the identification of the general PLSAs. It is a parameter-independent way directly used to identify the dynamic relation between the actuating pressures and the principal degrees of freedom of a PLSA, the bending and the steering. The approach is based on a modified auxiliary kinematic setting. Following the discussion of the identification procedure, the implementations for the double chamber bending and steering are demonstrated. The results show that the proposed approach can accurately capture the nonlinear pressure-shape dynamics. The approach is also efficient in the real-time applications. It can be further used to improve the current control design for the PLSAs in robotic applications.

There is a category of biped robots that are equipped with unactuated or passive ankles. We call them passive ankle walkers (PAWs). Because the unactuated ankle cannot provide the push-off at the end of stance phase as human ankles do, fast walking in PAWs is more challenging. In this paper, in order to realize fast walking in PAWs, we propose a simple strategy-torso inclination. To test this strategy, we studied a PAW model with simulation and prototype experiments. The simulation has shown how the torso inclination affects the walking speed and the energy efficiency of the PAW. Considering the “reality gap” problem of simulation, we have also experimentally tested this strategy with a real robot. By analyzing both the simulated model and the experimental results of the real robot, we identified the mechanism that accounts for fast walking in torso-inclined PAW.

In this paper the authors present the architecture of the planar biped robot "RunBot". It has been developed on the basis of three hierarchical levels: biomechanical, local and central. The biomechanical level concerns an appropriate biomechanical design of RunBot which utilizes some principles of passive walkers to ensure stability. The local level is a low-level neuronal structure which generates dynamically stable gaits as well as fast motions with some degree of self-stabilization to guarantee basic robustness. In the central level, we simulate a mechanism for synaptic plasticity which allows RunBot to autonomously learn to adapt its locomotion to different terrains, e.g. level floor versus up or down a ramp. As a result, the structural coupling of all these levels generates adaptive, fast dynamic walking of RunBot.

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori-motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (>3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks.

In Figure The incorrect Froude number given for human walking (0.24) corresponds to 1.5m/s, which is closer to the preferred speed of human walking. The correct number now given (2.4) corresponds to a speed of about 4.6m/s.

Biped walking remains a difficult problem, and robot models can greatly facilitate our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network governed mainly by local sensor signals. Building on earlier work (Taga, 1995; Cruse, Kindermann, Schumm, Dean, & Schmitz, 1998), this study shows that human-like gaits emerge without specific position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (anterior extreme angle and ground contact), which operate at the interjoint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e.g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motor neurons in our reflexive controller are directly driving the motors of the joints rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuromechanical system, and this study emphasizes that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using a real robot as well as by a Poincaré map analysis applied on a model of the robot in order to assess its stability.

Biologically inspired reflexive controllers have been implemented on various walking robots. However, due to the natural instability of biped walking, up to date, there has not existed a biped robot that depends exclusively on reflexive controllers for its dynamically stable walking control. In this paper, we present our design and experiments of a planar biped robot under control of a pure reflexive controller that includes only local extensor and flexor reflexes (no any other reflexes for explicit stability control). The reflexive controller is built with biologically inspired stretch receptors and model neurons. It requires fewer phasic feedbacks than those reflexive controllers of multilegged robots, and does not employ any kind of position or velocity control algorithm even on its low level. Instead, the approximate property of this reflexive controller has allowed our biped robot to substantially exploit its own passive dynamics in some stages of its walking gait cycle. Due to the interaction of the reflexive controller and the properly designed mechanics of the robot, the biped robot works as a closely coupled neuromechanical system, and demonstrates self-stabilizing property in the experiments of slightly perturbed walking, shallow slope walking, and various speed walking. Moreover, our biped robot can walk stably at a relatively high speed (nearly three leg-lengths per second). We know of no other biped robots that could attain such a high relative speed.

In this paper, we present our design and experiments of a planar biped robot ("RunBot") under pure reflexive neuronal control. The goal of this study is to combine neuronal mechanisms with biomechanics to obtain very fast speed and the on-line learning of circuit parameters. Our controller is built with biologically inspired sensor- and motor-neuron models, including local reflexes and not employing any kind of position or trajectory-tracking control algorithm. Instead, this reflexive controller allows RunBot to exploit its own natural dynamics during critical stages of its walking gait cycle. To our knowledge, this is the first time that dynamic biped walking is achieved using only a pure reflexive controller. In addition, this structure allows using a policy gradient reinforcement learning algorithm to tune the parameters of the reflexive controller in real-time during walking. This way RunBot can reach a relative speed of 3.5 leg-lengths per second after a few minutes of online learning, which is faster than that of any other biped robot, and is also comparable to the fastest relative speed of human walking. In addition, the stability domain of stable walking is quite large supporting this design strategy.

A type of planar one-legged robot is proposed which, unlike a previous one-legged robot with springy legs, consists of three revolute joints, two links and two feet. In Geng et al. (2001), a cyclic jumping gait has been designed for this robot, which required that angular rate of the knee joint be zero at the instant of takeoff and that the knee joint be locked during flight phases. To eliminate these two requirements that were difficult to implement; a different ballistic flipping gait is designed for the robot in this paper. A complete flipping gait cycle is composed of four phases: two stance phases and two flight phases. During flight phases, torque of the knee joint is zero, the total angular momentum with respect to the center of mass of the system is conserved, rotational motion and translational motion is decoupled from each other. Trajectory planning of the four phases is formulated as a problem of numerical optimization subject to nonlinear constraints such as positive reaction force of ground and finite torque of the joints. Energy injected into the robot in a gait cycle is chosen as a criterion of optimization in the simulation.

A novel construction of planar one-legged robot is proposed, which, unlike previous one-legged robot with springy legs, consists of three revolute joints, two links and two feet. On some conditions, it can be modeled as a two-degree-of-freedom manipulator. Inspired by our observation of a jumping frog, we design a cyclic gait for this one-legged robot. The gait is divided into four phases. During takeoff only two phases are actively controlled and exerted on the knee joint. The trajectory planning of the four phases is formalised as a problem of optimization subject to several constraints, including the ZMP constraint.