1,721,110 research outputs found
Soft Bilinear Inverted Pendulum: A Model to Enable Locomotion With Soft Contacts
No full textThe robotics research community has developed several effective techniques for quadrupedal locomotion. Most of these methods ease the modeling and control problem by assuming a rigid contact between the feet and the terrain. However, in the case of compliant terrain or robots equipped with soft feet, this assumption no longer holds, as the contact point moves and the reaction forces experience a delay. This article presents a novel approach for quadrupedal locomotion in the presence of soft contacts. The control architecture consists of two blocks: 1) upstream, the motion planner (MP) computes a feasible trajectory using model predictive control (MPC) and 2) downstream, the tracking controller (TC) employs hierarchical optimization (HO) to achieve motion tracking. This choice allows the control architecture to employ a large time horizon without heavily compromising the model's accuracy. For the first time, both blocks consider the contact compliance: in the MP, the classic linear inverted pendulum model is extended by proposing the soft bilinear inverted pendulum (SBIP) model; conversely, the TC is a whole-body controller (WBC) that considers the full dynamics model, including the soft contacts. Simulations with multiple quadrupedal robots demonstrate that the proposed approach enables traversing soft terrains with improved stability and efficiency. Furthermore, the performance benefits of including the compliance in the MP and TC are evaluated. Finally, experiments on the SOLO12 robot walking on soft terrain validate the proposed approach's effectiveness
TraQuad: A Modular Tracked Legged Multimodal Quadrupedal Robot
No full textThe authors have developed a novel multimodal robot named TraQuad, which integrates the features of legged and tracked robots. This robot aims to combine agility, maneuverability, traction, and efficiency for traversing various environments. Legged locomotion allows the robot to select optimal contact points on the terrain, while tracked locomotion enables faster movement over relatively simpler uneven terrains with greater efficiency. TraQuad can turn about its central vertical axis and execute sharp turns with a 0.25 m turn radius. It can climb steep slopes of 31∘ at a velocity of 0.9 m/s. Utilizing multimodal locomotion, it can climb rocks and overcome obstacles by either skipping or stepping on them. Climbing rocks 1.75 times the height of the tracks requires a peak torque of 5.14 N⋅m, whereas stepping on a block of the same height requires a peak torque of 8.15 N⋅m. Skipping a block 1.5 times the height of tracks requires a peak torque of 11.8 N⋅m. This demonstrates that climbing obstacles while maintaining contact with them is more economical than stepping on them, proving the viability of tracked-legged locomotion. These advancements highlight the potential of TraQuad as a robust solution for navigating diverse and challenging environments
OmniQuad: A wheeled-legged hybrid robot with omnidirectional wheels
No full textCombining wheeled and legged locomotion within a single robotic platform offers the potential to harness the advantages of both systems. The development of such hybrid systems remains an active area of research. While legged robots exhibit remarkable adaptability for traversing uneven and rugged terrains, their relatively low speed limits their practicality in indoor environments. Conversely, wheeled robots demonstrate higher efficiency on flat surfaces but often encounter difficulties when navigating obstacles such as steps. This paper introduces OmniQuad, a novel hybrid robot that integrates mecanum wheels with legged locomotion to exploit the benefits of both modalities. Mecanum wheels provide omnidirectional mobility, enhancing the robot's maneuvrability by enabling agile movement in confined spaces and the ability to maintain a consistent orientation during task execution. In this work, we first present the design of a custom wheeled-foot featuring mecanum wheels. Subsequently, we describe the complete robot assembly, comprising these wheeled feet and EM-Act actuators. Finally, we detail the control scheme developed to govern the OmniQuad and evaluate its performance through experimental trials conducted in both indoor and outdoor environments
Swing-Up of Underactuated Compliant Arms via Iterative Learning Control
Full text linkThe swing-up is a classical problem of control theory that has already been widely studied in the literature. Despite that, swinging up an underactuated compliant arm considerably increases the problem complexity. Indeed, in addition to the problem of underactuation, compliant systems usually present also hard-to-model dynamics. Moreover, the control authority of feedback components should be limited to avoid radical alteration of the robot natural elasticity. In this letter, we tackle the swing-up problem of underactuated compliant arms via an Iterative Learning Control approach, proving its convergence. The proposed control law combines feedforward and feedback terms. Tracking the desired trajectory, i.e., bringing the robot up to its vertical equilibrium, is achieved thanks to the feedforward components. Conversely, the feedback of the output signal is used to stabilize the system at the equilibrium point. Additionally, we study the stiffness variation caused by the employed feedback, deriving a condition to preserve the elasticity of the compliant arm. Finally, we validate the proposed method via simulations and experiments underactuated compliant arms with unstable vertical equilibrium varying number of unactuated joints, payload, stiffness, model uncertainties, and noise
Learning-Based Foot-Shape-Aware Foothold Selection for Quadrupedal Robots
No full textMastering rough terrain locomotion is a tough challenge for robots due to its dynamic, unpredictable nature and frequent physical contact. Traditionally, robots rely on carefully planned foot placements to maintain grip and stability. Recent advancements in quadruped robot feet offer diverse shapes and high grip for various terrains. However, control systems and planners often struggle to leverage these varied capabilities, relying instead on simplified foot models e.g., ball-like, flat. The simplified feet models committed to the single shape of the foot can not be used on robots equipped with diverse feet or modern adaptive feet. This work proposes a novel foothold optimization method that efficiently searches for optimal contact points for different foot shapes using a polynomial approximation. The system leverages a Convolutional Neural Network (CNN) trained on simulated data to predict a cost for each candidate foothold. We show that a single neural network can work with different and new foot mechanical designs without retraining the system. We experimentally validate our system on the ANYmal robot using both ball feet and adaptive soft feet, in indoor and outdoor environments, finding that our system improves stability, in terms of pitch and roll angles of the base, with respect to a state-of-the-art method
Control Architecture for Human-Like Motion With Applications to Articulated Soft Robots
Full text linkHuman beings can achieve a high level of motor performance that is still unmatched in robotic systems. These capabilities can be ascribed to two main enabling factors: (i) the physical proprieties of human musculoskeletal system, and (ii) the effectiveness of the control operated by the central nervous system. Regarding point (i), the introduction of compliant elements in the robotic structure can be regarded as an attempt to bridge the gap between the animal body and the robot one. Soft articulated robots aim at replicating the musculoskeletal characteristics of vertebrates. Yet, substantial advancements are still needed under a control point of view, to fully exploit the new possibilities provided by soft robotic bodies. This paper introduces a control framework that ensures natural movements in articulated soft robots, implementing specific functionalities of the human central nervous system, i.e., learning by repetition, after-effect on known and unknown trajectories, anticipatory behavior, its reactive re-planning, and state covariation in precise task execution. The control architecture we propose has a hierarchical structure composed of two levels. The low level deals with dynamic inversion and focuses on trajectory tracking problems. The high level manages the degree of freedom redundancy, and it allows to control the system through a reduced set of variables. The building blocks of this novel control architecture are well-rooted in the control theory, which can furnish an established vocabulary to describe the functional mechanisms underlying the motor control system. The proposed control architecture is validated through simulations and experiments on a bio-mimetic articulated soft robot.Learning & Autonomous Contro
Drum Stroke Variation Using Variable Stiffness Actuators
Full text linkOne interesting field of robotics technology is related to the entertainment industry. Performing a musical piece using a robot is a difficult task because music presents many features like melody, rhythm, tone, harmony and so on. Addressing these tasks with a robot is not trivial to implement. Most of approaches which related to this specific field lacks of quality to perform in front of human audience. Implementation of human-like motions can not be properly achieved with a conventional robot actuator. Consequently, we exploit a new type of actuator which simplifies the drawbacks of a conventional one. We used Variable Stiffness Actuator(VSA) instead of using conventional actuator. We can control position, force, and stiffness, simultaneously by using VSA. The most important novel feature is its controllable stiffness. When the stiffness of the actuator is changed, the characteristics of the actuator's response also changes. We implemented the specific stroke which is called “double stroke” using one of variable stiffness actuator. Although the double stroke is known as a special stroke which could be performed by human only, double stroke is successfully implemented by stiffness variation
Soft actuation in cyclic motions: Stiffness profile optimization for energy efficiency
Full text linkIn this paper, we investigate the role of variable stiffness in the reduction of the energy cost for mechanical systems that perform desired tasks. The objective is to assess the use of Variable Stiffness Actuation (VSA) by determining an optimal stiffness profile and the associated energy cost of performing a desired task. For the analysis we consider mechanical systems of n-Degrees of Freedom (DoF), using VSA. We find an analytical solution that expresses the optimal stiffness profile during the task as a function of joint trajectories. This stiffness profile can be either constant or variable in time, and it minimizes a cost function, when performing a desired task. We calculate the cost related to the torque of the system and the additional cost of changing or keeping a stiffness actively constant. Additionally, we discuss some cases for which it is worth to change the stiffness during a task and cases for which a constant stiffness may be better solution. Furthermore, from simulations and experiments we show cases in which using a variable stiffness profile allows cost savings w.r.t. constant stiffness. The use of variable stiffness depends on the task, i.e. on the joint trajectories and their frequency, as well as on the mechanical implementation of the actuator used
On the motion/stiffness decoupling property of articulated soft robots with application to model-free torque iterative learning control
Full text linkThis paper tackles the problem of controlling articulated soft robots (ASRs), i.e., robots with either fixed or variable elasticity lumped at the joints. Classic control schemes rely on high-authority feedback actions, which have the drawback of altering the desired robot softness. The problem of accurate control of ASRs, without altering their inherent stiffness, is particularly challenging because of their complex and hard-to-model nonlinear dynamics. Leveraging a learned anticipatory action, Iterative Learning Control (ILC) strategies do not suffer from these issues. Recently, ILC was adopted to perform position control of ASRs. However, the limitation of position-based ILC in controlling variable stiffness robots is that whenever the robot stiffness profile is changed, a different input action has to be learned. Our first contribution is to identify a wide class of ASRs, whose motion and stiffness adjusting dynamics can be proved to be decoupled. This class is described by two properties that we define: strong elastic coupling - relative to motors and links of the system, and their connections - and homogeneity - relative to the characteristics of the motors. Furthermore, we design a torque-based ILC scheme that, starting from a rough estimation of the system parameters, refines the torque needed for the joint positions tracking. The resulting control scheme requires minimum knowledge of the system. Experiments on variable stiffness robots prove that the method effectively generalizes the iterative procedure w.r.t. the desired stiffness profile and allows good tracking performance. Finally, potential restrictions of the method, e.g., caused by friction phenomena, are discussed
Optimality Principles in Stiffness Control: The VSA Kick
No full textThe importance of Variable Stiffness Actuators (VSA) in safety and performance of robots has been extensively discussed in the last decade. It has also been shown recently that a VSA brings performance advantages with respect to common actuators. For instance, the solution of the optimal control problem of maximizing the speed of a VSA for impact maximization at a given position with free final time is achieved by applying a control policy that synchronizes stiffness changes with link speed and acceleration. This problem can be regarded as the formalization of the performance of a soccer player's free kick
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