This paper is concerned with the design of a control strategy for variable stiffness actuators in series configuration, exploiting the lever concept to adjust the stiffness at the transmission. A control strategy based on gain scheduling is proposed, which is able to regulate both stiffness and position at output link. The gain scheduling is designed based on a set of linear quadratic regulators (LQRs), because LQR's inherent robustness properties can accommodate significant variation in the actuation plant parameters. The link positioning relies on continuous adjustment of the control effort based on the current transmission stiffness; the stiffness perceived at the output link is regulated through combined action of the transmission stiffness and the positioning gains of the scheduling strategy. The effectiveness of the controller is verified in simulation and experiments on the actuator with adjustable stiffness. The overall strategy has been proven to be locally stable.

Variable stiffness actuators have been developed based on different design solutions which can be arranged into two groups: antagonistic and series design. In both the cases two actuation units are combined with passive elastic elements to adjust both the stiffness and the equilibrium position of the actuated joint. To regulate the stiffness, mechanical work is required to be done which depending on the design principle of the actuator results in certain energy consumption. In this paper different variable stiffness design approaches with different types of springs (linear, quadratic, exponential and cubic) are analyzed and compared with respect to the energy required to regulate the stiffness. The results give some insights about the design parameters which mostly affect the energy consumption for the stiffness adjustment. In this work, it is shown that among different design and spring arrangements, the variable stiffness in series design which uses linear springs with constant pretension, requires the minimum energy consumption to adjust the stiffness.

Variable stiffness actuators (VSAs) have been introduced to improve, at the design level, the safety and the energy efficiency of the new generation of robots that have to interact closely with humans. A wide variety of design solutions have recently been proposed, and a common factor in most of the VSAs is the introduction of a flexible transmission with varying stiffness. This, from the control perspective, usually implies a nonlinear actuation plant with varying dynamics following time-varying parameters, which requires more complex control strategies with respect to those developed for flexible joints with a constant stiffness. For this reason, this paper proposes an approach for controlling the link position and stiffness of a VSA. The link positioning relies on a LQR-based gain scheduling approach useful for continuously adjusting the control effort based on the current stiffness of the flexible transmission. The stiffness perceived at the output link is adjusted to match the varying task requirements through the combination of the positioning gains and the mechanical stiffness. The stability of the overall strategy is briefly discussed. The effectiveness of the controller in terms of tracking performance and stiffness adjustment is verified through experiments on the Actuator with Adjustable Stiffness (AwAS).

This paper focuses on the estimation of the impedance for a Variable Impedance Actuator (VIA) through torque and position measurements. Despite the recent development of several VIA, impedance control is not yet implemented in closed loop because of the difficulty of obtaining in real-time measurements of time-varying impedance. The estimation algorithm is proposed as an alternative approach to the standard procedures of impedance identification, to robustly tolerate the variability of the mechanical stiffness due, for example, to model uncertainties. The impedance estimator is therefore implemented on the Actuator with Adjustable Stiffness (AwAS). The effectiveness of the proposed estimator is proved through simulation and experimental results.

Affine-invariant feature matching plays an important role in many robot vision applications, such as robot visual navigation, object detection, visual tracking and visual SLAM, etc. In the early stages, invariant keypoints are used to detect the affine transformation. But the accuracy is very low. In recent years, some people introduce SIFT method into robot vision field, which greatly enhances the accuracy. But it is too time-consuming to meet the requirements of real-time robot vision applications. In this paper, we propose a novel learning-based feature matching approach to address the problem. First, it uses a fast algorithm to extract keypoints. Then, our method identifies keypoints that belong to different objects or background by color and texture representation. The keypoints are clustered into corresponding groups. At last, a two-stage multilayer ferns classifier is trained to recognize the local patches and get the estimate of viewpoint. We test our approach on public datasets and apply it in a visual SLAM application. The result demonstrates that our method can provide robust and powerful matching ability. Even on some difficult matching cases, it also performs remarkably well. Further more, because there is no need to compute descriptors for the image, our method is very fast at run-time.

This paper describes the design and modelling of a new variable stiffness actuator (CompAct-VSA). The principle of operation of CompAct-VSA is based on a lever arm mechanism with a continuously regulated pivot point. The proposed concept allows for the development of an actuation unit with a wide range of stiffness and a fast stiffness regulation response. The implementation of the actuator makes use of a cam shaped lever arm with a variable pivot axis actuated by a rack and pinion transmission system. This realization results in a highly integrated and modular assembly. Size and weight are indeed an open issue in the VSAs design, which ultimately limit their implementation in multi-dof robotic systems. The paper introduces the mechanics, the principle of operation and the model of the actuator. Preliminary results are presented to demonstrate the fast stiffness regulation response and the wide range of stiffness achieved by the proposed CompAct-VSA design.

We propose a control strategy for a robotic manipulator operating in an unstructured environment while interacting with a human operator. The proposed system takes into account the important characteristics of the task and the redundancy of the robot to determine a controller that is safe for the user. The constraints of the task are first extracted using several examples of the skill demonstrated to the robot through kinesthetic teaching. An active control strategy based on task-space control with variable stiffness is proposed, and combined with a safety strategy for tasks requiring humans to move in the vicinity of robots. A risk indicator for human-robot collision is defined, which modulates a repulsive force distorting the spatial and temporal characteristics of the movement according to the task constraints. We illustrate the approach with two human-robot interaction experiments, where the user teaches the robot first how to move a tray, and then shows it how to iron a napkin.

The current research effort in the design of lightweight and safe robots is resulting in increased interest for the development of variable stiffness actuators. Antagonistic pneumatic muscle actuators (pMAs) have been proposed for this purpose, due to their inherent nonlinear spring behavior resulting from both air compressibility and their nonlinear force-length relation. This paper addresses the simultaneous torque and stiffness control of an antagonistically actuated joint with pneumatic muscles driven by compact, fast-switching solenoid valves. This strategy allows compensation of unmodeled joint dynamics while adjusting the joint stiffness depending on the task requirements. The proposed controller is based on a sliding mode force control applied to an average model of the valve-pneumatic muscle system. This was necessary to cope with both the well known model uncertainties of the pMA and the discontinuous on-off behavior of the solenoid valves. Preliminary experimental results verified the effectiveness of the proposed implementation.

Safety is a critical characteristic for robots designed to operate in human environments. This paper presents the concept of hybrid actuation for the development of human-friendly robotic systems. The new design employs inherently safe pneumatic artificial muscles augmented with small electrical actuators, human-bone-inspired robotic links, and newly designed distributed compact pressure regulators. The modularization and integration of the robot components enable low complexity in the design and assembly. The hybrid actuation concept has been validated on a two-degree-of-freedom prototype arm. The experimental results show the significant improvement that can be achieved with hybrid actuation over an actuation system with pneumatic artificial muscles alone. Using the Manipulator Safety Index (MSI), the paper discusses the safety of the new prototype and shows the robot arm safety characteristics to be comparable to those of a human arm.

Recently, on the base of distributed macro-mini actuation approach (DM2), a new robotic manipulator with hybrid actuation, air muscles-DC motor, has been developed. Among existing actuators, the hybrid actuation employs air muscles because they represent an advantageous tradeoff of performance and safety, due to their power/weight ratio and inherent compliance. The air muscles, however, are limited in bandwidth and their behavior is highly nonlinear. In order to overcome these limitations, the paper presents a torque control strategy based on a pair of differentially connected force-controlled air muscles. This controller was implemented and evaluated on a single joint testbed, first by itself and then as macro component into the Macro-Mini control strategy.