A robust iterative learning control for continuous-time nonlinear systems with disturbances

In this paper, we study the trajectory tracking problem using iterative learning control for continuous-time nonlinear systems with a generic fixed relative degree in the presence of disturbances. This class of controllers iteratively refine the control input relying on the tracking error of the previous trials and some properly tuned learning gains. Sufficient conditions on these gains guarantee the monotonic convergence of the iterative process. However, the choice of the gains is heuristically hand-tuned given an approximated system model and no information on the disturbances. Thus, in the cases of inaccurate knowledge of the model or iteration-varying measurement errors, external disturbances, and delays, the convergence condition is unlikely to be verified at every iteration. To overcome this issue, we propose a robust convergence condition, which ensures the applicability of the pure feedforward control even if other classical conditions are not fulfilled for some trials due to the presence of disturbances. Furthermore, we quantify the upper bound of the nonrepetitive disturbance that the iterative algorithm is able to handle. Finally, we validate the convergence condition simulating the dynamics of a two degrees of freedom underactuated arm with elastic joints, where one is active, and the other is passive, and a Franka Emika Panda manipulator

Grasping with Soft Hands

Despite some prematurely optimistic claims, the ability of robots to grasp general objects in unstructured environments still remains far behind that of humans. This is not solely caused by differences in the mechanics of hands: indeed, we show that human use of a simple robot hand (the Pisa/IIT SoftHand) can afford capabilities that are comparable to natural grasping. It is through the observation of such human-directed robot hand operations that we realized how fundamental in everyday grasping and manipulation is the role of hand compliance, which is used to adapt to the shape of surrounding objects. Objects and environmental constraints are in turn used to functionally shape the hand, going beyond its nominal kinematic limits by exploiting structural softness. In this paper, we set out to study grasp planning for hands that are simple - in the sense of low number of actuated degrees of freedom (one for the Pisa/IIT SoftHand) - but are soft, i.e. continuously deformable in an infinity of possible shapes through interaction with objects. After general considerations on the change of paradigm in grasp planning that this setting brings about with respect to classical rigid multi-dof grasp planning, we present a procedure to extract grasp affordances for the Pisa/IIT SoftHand through physically accurate numerical simulations. The selected grasps are then successfully tested in an experimental scenario

Open Source VSA-CubeBots for Rapid Soft Robot Prototyping

Nowadays, rapid robot prototyping is a desired capability of any robotics laboratory. Combining the speed of 3D plastic printing and the use of custom Open Source electronic hardware/software solutions, our laboratory successfully developed and used tools related to variable impedance robot technology. This paper describes how we capitalized the design and use of one kind of variable stiffness actuators as a modular tool to prototype and test in a quick fashion several robot capabilities. The extension of such a modular tool for rapid prototyping allowed us to use it in several applications and scenarios, including the educational setting, aiming to speed up the gap between theory and practice in robotics. The complete palette of developments of our laboratory in hardware/software as well as some robotic systems applications shown here, are open source and contribute to the Natural Motion Initiative

Variable stiffness control for oscillation damping

In this paper a model-free approach for damping control of Variable Stiffness Actuators is proposed. The idea is to take advantage of the possibility to change the stiffness of the actuators in controlling the damping. The problem of minimizing the terminal energy for a one degree of freedom spring-mass model with controlled stiffness is first considered. The optimal bang-bang control law uses a maximum stiffness when the link gets away from the desired position, i.e. the link velocity is decreasing, and a minimum one when the link is going towards it, i.e. the link velocity is increasing. Based on Lyapunov stability theorems the obtained law has been proved to be stable for a multi-DoF system. Finally, the proposed control law has been tested and validated through experimental tests

Optimal control and design guidelines for soft jumping robots: Series elastic actuation and parallel elastic actuation in comparison

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Potential merits for space robotics from novel concepts of actuation for soft robotics

Autonomous robots in dynamic and unstructured environments require high performance, energy efficient and reliable actuators. In this paper we give an overview of the first results of two lines of research regarding the novel actuation principle we introduced: Series-Parallel Elastic Actuation (SPEA). Firstly, we introduce the SPEA concept and present first prototypes and results. Secondly, we discuss the potential of self-healing materials in robotics, and discuss the results on the first self-healing pneumatic cell and selfhealing mechanical fuse. Both concepts have the potential to improve performance, energy efficiency and reliability

Iterative Learning Control as a Framework for Human-Inspired Control with Bio-mimetic Actuators

The synergy between musculoskeletal and central nervous systems empowers humans to achieve a high level of motor performance, which is still unmatched in bio-inspired robotic systems. Literature already presents a wide range of robots that mimic the human body. However, under a control point of view, substantial advancements are still needed to fully exploit the new possibilities provided by these systems. In this paper, we test experimentally that an Iterative Learning Control algorithm can be used to reproduce functionalities of the human central nervous system - i.e. learning by repetition, after-effect on known trajectories and anticipatory behavior - while controlling a bio-mimetically actuated robotic arm

Robotic Monitoring of Habitats: the Natural Intelligence Approach

In this paper, we first discuss the challenges related to habitat monitoring and review possible robotic solutions. Then, we propose a framework to perform terrestrial habitat monitoring exploiting the mobility of legged robotic systems. The idea is to provide the robot with the Natural Intelligence introduced as the combination of the environment in which it moves, the intelligence embedded in the design of its body, and the algorithms composing its mind. This approach aims to solve the challenges of deploying robots in real natural environments, such as irregular and rough terrains, long-lasting operations, and unexpected collisions, with the final objective of assisting humans in assessing the habitat conservation status. Finally, we present examples of robotic monitoring of habitats in four different environments: forests, grasslands, dunes, and screes