Robust Whole-Body Motion Control of Legged Robots

We introduce a robust control architecture for the whole-body motion control of torque controlled robots with arms and legs. The method is based on the robust control of contact forces in order to track a planned Center of Mass trajectory. Its appeal lies in the ability to guarantee robust stability and performance despite rigid body model mismatch, actuator dynamics, delays, contact surface stiffness, and unobserved ground profiles. Furthermore, we introduce a task space decomposition approach which removes the coupling effects between contact force controller and the other non-contact controllers. Finally, we verify our control performance on a quadruped robot and compare its performance to a standard inverse dynamics approach on hardware.Comment: 8 Page

Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges

Continuum soft robots are mechanical systems entirely made of continuously deformable elements. This design solution aims to bring robots closer to invertebrate animals and soft appendices of vertebrate animals (e.g., an elephant's trunk, a monkey's tail). This work aims to introduce the control theorist perspective to this novel development in robotics. We aim to remove the barriers to entry into this field by presenting existing results and future challenges using a unified language and within a coherent framework. Indeed, the main difficulty in entering this field is the wide variability of terminology and scientific backgrounds, making it quite hard to acquire a comprehensive view on the topic. Another limiting factor is that it is not obvious where to draw a clear line between the limitations imposed by the technology not being mature yet and the challenges intrinsic to this class of robots. In this work, we argue that the intrinsic effects are the continuum or multi-body dynamics, the presence of a non-negligible elastic potential field, and the variability in sensing and actuation strategies.Comment: 69 pages, 13 figure

Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining

This study evaluates the machining performance of newly developed modified jatropha oils (MJO1, MJO3 and MJO5), both with and without hexagonal boron nitride (hBN) particles (ranging between 0.05 and 0.5 wt%) during turning of AISI 1045 using minimum quantity lubrication (MQL). The experimental results indicated that, viscosity improved with the increase in MJOs molar ratio and hBN concentration. Excellent tribological behaviours is found to correlated with a better machining performance were achieved by MJO5a with 0.05 wt%. The MJO5a sample showed the lowest values of cutting force, cutting temperature and surface roughness, with a prolonged tool life and less tool wear, qualifying itself to be a potential alternative to the synthetic ester, with regard to the environmental concern

Dynamics and Control of Fiber-Elastomer Composites embedded with Shape Memory Alloys

Soft robots have been used in a wide range of applications from robotic and mechanical engineering to medicine and biomededical field. The growing interest in soft robots comes from their good performance in environments which is not best suited for conventional rigid bodies. Soft robots utilize the compliance, adaptability and flexibility of soft materials and actuation methods to develop highly adaptive structures. Among the soft materials, elastomers are specially popular due to their wide range of elasticity and viscoelasticity. Along with elastomers, textile fabrics are also of high interest for soft robotic applications due to their bendable, flexible, and often stretchable nature. The reinforcement of elastomers with textile fibers results in so-called integrated fiber-elastomer composites (IFEC) which offer a wide variety of properties such as flexibility, strength, fracture toughness and damage resistance. The elastic properties of textile reinforced composites require smart actuators which possess adaptability and deformability. Among existing smart actuators, shape memory alloys (SMA) have been frequently adopted in flexible structures including soft robots. SMAs have sensing and actuation capabilities and are characterized by flexibility and lightness which facilitates their integration into these structures. In this dissertation, the modeling and control of soft prototypes made of IFEC are presented. Shape memory alloys are embedded in the composites for the system actuation. First, the mechanical design and production of three IFEC prototypes are described. For each prototype, a test bench including power and control electronics set-up is designed. Next, mathematical models are developed to analyze the dynamic behavior of the prototypes. The IFEC systems exhibit highly nonlinear behaviour due to SMA hysteresis. For modeling, two different approaches, namely physical modelling and system identification are adopted. In physical modeling, the SMA constitutive and heat transfer equations are incorporated with the composite deflection model. To fully develop the equations, thermal and mechanical parameters of SMA wires are identified experimentally. In the second approach, the mathematical model of the systems is derived from experimental identification and unstructured uncertainty models. Two different control techniques are proposed to compensate the nonlinear behavior of the systems and ensure a robust, fast and precise position tracking. In the first control technique, a proportional integral (PI) controller is designed through robust stability analysis. The second controller is a multivariable PI control which is designed for the prototypes that can move in more than one direction. The performance of the controllers are examined experimentally

An adaptive hierarchical control for aerial manipulators

This paper addresses the trajectory tracking control problem for a quadrotor aerial vehicle, equipped with a robotic manipulator (aerial manipulator). The controller is organized in two layers: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables; in the bottom layer, a motion control algorithm is in charge of tracking the motion references computed by the upper layer. To the purpose, a model-based control scheme is adopted, where modelling uncertainties are compensated through an adaptive term. The stability of the proposed scheme is proven by resorting to Lyapunov arguments. Finally, a simulation case study is proposed to prove the effectiveness of the approach

Application of a differentiator-based adaptive super-twisting controller for a redundant cable-driven parallel robot

In this paper we present preliminary, experimental results of an Adaptive Super-Twisting Sliding-Mode Controller with time-varying gains for redundant Cable-Driven Parallel Robots. The sliding-mode controller is paired with a feed-forward action based on dynamics inversion. An exact sliding-mode differentiator is implemented to retrieve the velocity of the end-effector using only encoder measurements with the properties of finite-time convergence, robustness against perturbations and noise filtering. The platform used to validate the controller is a robot with eight cables and six degrees of freedom powered by 940 W compact servo drives. The proposed experiment demonstrates the performance of the controller, finite-time convergence and robustness in tracking a trajectory while subject to external disturbances up to approximately 400% the mass of the end-effector

Motion Planning and Control of Dynamic Humanoid Locomotion

Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot. Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d. As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics. The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end