On the role of robot configuration in Cartesian stiffness control

The stiffness ellipsoid, i.e. the locus of task-space forces obtained corresponding to a deformation of unit norm in different directions, has been extensively used as a powerful representation of robot interaction capabilities. The size and shape of the stiffness ellipsoid at a given end-effector posture are influenced by both joint control parameters and - for redundant manipulators - by the chosen redundancy resolution configuration. As is well known, impedance control techniques ideally provide control parameters which realize any desired shape of the Cartesian stiffness ellipsoid at the end-effector in an arbitrary non-singular configuration, so that arm geometry selection could appear secondary. This definitely contrasts with observations on how humans control their arm stiffness, who in fact appear to predominantly use arm configurations to shape the stiffness ellipsoid. To understand this discrepancy, we provide a more complete analysis of the task-space force/deformation behavior of redundant arms, which explains why arm geometry also plays a fundamental role in interaction capabilities of a torque controlled robot. We show that stiffness control of realistic robot models with bounds on joint torques can't indeed achieve arbitrary stiffness ellipsoids at any given arm configuration. We first introduce the notion of maximum allowable Cartesian force/displacement (“stiffness feasibility”) regions for a compliant robot. We show that different robot configurations modify such regions, and explore the role of different configurations in defining the performance limits of Cartesian stiffness controllers. On these bases, we design a stiffness control method that suitably exploits both joint control parameters and redundancy resolution to achieve desired task-space interaction behavior

A Method for Autonomous Robotic Manipulation through Exploratory Interactions with Uncertain Environments

Expanding robot autonomy can deliver functional flexibility and enable fast deployment of robots in challenging and unstructured environments. In this direction, significant advances have been recently made in visual-perception driven autonomy, which is mainly due to the availability of rich sensory data-sets. However, current robots’ physical interaction autonomy levels still remain at a basic level. Towards providing a systematic approach to this problem, this paper presents a new context-aware and adaptive method that allows a robotic platform to interact with unknown environments. In particular, a multi-axes self-tuning impedance controller is introduced to regulate quasi-static parameters of the robot based on previous experience in interacting with similar environments and the real-time sensory data. The proposed method is also capable of differentiating internal and external disruptions, and responding to them accordingly and appropriately. An agricultural experiment with different deformable material is presented to validate robot interaction autonomy improvements, and the capability of the proposed methodology in detecting and responding to unexpected events (e.g., faults)

Human-Like Impedance and Minimum Effort Control for Natural and Efficient Manipulation

Humans incorporate and switch between learnt neuromotor strategies while performing complex tasks. Towards this purpose, kinematic redundancy is exploited in order to achieve optimized performance. Inspired by the superior motor skills of humans, in this paper, we investigate a combined free motion and interaction controller in a certain class of robotic manipulation. In this bimodal controller, kinematic degrees of redundancy are adapted according to task-suitable dynamic costs. The proposed algorithm attributes high priority to minimum-effort controller while performing point to point free space movements. Once the robot comes in contact with the environment, the Tele-Impedance, common mode and configuration dependent stiffness (CMS-CDS) controller will replicate the human’s estimated endpoint stiffness and measured equilibrium position profiles in the slave robotic arm, in real-time. Results of the proposed controller in contact with the environment are compared with the ones derived from Tele-Impedance implemented using torque based classical Cartesian stiffness control. The minimum-effort and interaction performance achieved highlights the possibility of adopting human-like and sophisticated strategies in humanoid robots or the ones with adequate degrees of redundancy, in order to accomplish tasks in a certain class of robotic manipulatio

Towards Autonomous Robotic Valve Turning

In this paper an autonomous intervention robotic task to learn the skill of grasping and turning a valve is described. To resolve this challenge a set of different techniques are proposed, each one realizing a specific task and sending information to the others in a Hardware-In-Loop (HIL) simulation. To improve the estimation of the valve position, an Extended Kalman Filter is designed. Also to learn the trajectory to follow with the robotic arm, Imitation Learning approach is used. In addition, to perform safely the task a fuzzy system is developed which generates appropriate decisions. Although the achievement of this task will be used in an Autonomous Underwater Vehicle, for the first step this idea has been tested in a laboratory environment with an available robot and a sensor

Performance Analysis of Vibrotactile and Slide-and-Squeeze Haptic Feedback Devices for Limbs Postural Adjustment

Recurrent or sustained awkward body postures are among the most frequently cited risk factors to the development of work-related musculoskeletal disorders (MSDs). To prevent workers from adopting harmful configurations but also to guide them toward more ergonomic ones, wearable haptic devices may be the ideal solution. In this paper, a vibrotactile unit, called ErgoTac, and a slide-and-squeeze unit, called CUFF, were evaluated in a limbs postural correction setting. Their capability of providing single-joint (shoulder or knee) and multi-joint (shoulder and knee at once) guidance was compared in twelve healthy subjects, using quantitative task-related metrics and subjective quantitative evaluation. An integrated environment was also built to ease communication and data sharing between the involved sensor and feedback systems. Results show good acceptability and intuitiveness for both devices. ErgoTac appeared as the suitable feedback device for the shoulder, while the CUFF may be the effective solution for the knee. This comparative study, although preliminary, was propaedeutic to the potential integration of the two devices for effective whole-body postural corrections, with the aim to develop a feedback and assistive apparatus to increase workers' awareness about risky working conditions and therefore to prevent MSDs

Garbage Collection and Sorting with a Mobile Manipulator using Deep Learning and Whole-Body Control

Domestic garbage management is an important aspect of a sustainable environment. This paper presents a novel garbage classification and localization system for grasping and placement in the correct recycling bin, integrated on a mobile manipulator. In particular, we first introduce and train a deep neural network (namely, GarbageNet) to detect different recyclable types of garbage. Secondly, we use a grasp localization method to identify a suitable grasp pose to pick the garbage from the ground. Finally, we perform grasping and sorting of the objects by the mobile robot through a whole-body control framework. We experimentally validate the method, both on visual RGB-D data and indoors on a real full-size mobile manipulator for collection and recycling of garbage items placed on the ground

Exploring Teleimpedance and Tactile Feedback for Intuitive Control of the Pisa/IIT SoftHand

This paper proposes a teleimpedance controller with tactile feedback for more intuitive control of the Pisa/IIT SoftHand. With the aim to realize a robust, efficient and low-cost hand prosthesis design, the SoftHand is developed based on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. Due to the built-in flexibility of the hand joints, as the SoftHand grasps, it follows a synergistic path while allowing grasping of objects of various shapes using only a single motor. The DC motor of the hand incorporates a novel teleimpedance control in which the user's postural and stiffness synergy references are tracked in real-time. In addition, for intuitive control of the hand, two tactile interfaces are developed. The first interface (mechanotactile) exploits a disturbance observer which estimates the interaction forces in contact with the grasped object. Estimated interaction forces are then converted and applied to the upper arm of the user via a custom made pressure cuff. The second interface employs vibrotactile feedback based on surface irregularities and acceleration signals and is used to provide the user with information about the surface properties of the object as well as detection of object slippage while grasping. Grasp robustness and intuitiveness of hand control were evaluated in two sets of experiments. Results suggest that incorporating the aforementioned haptic feedback strategies, together with user-driven compliance of the hand, facilitate execution of safe and stable grasps, while suggesting that a low-cost, robust hand employing hardware-based synergies might be a good alternative to traditional myoelectric prostheses

The sensor-based biomechanical risk assessment at the base of the need for revising of standards for human ergonomics

Due to the epochal changes introduced by “Industry 4.0”, it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human–robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative “direct instrumental evaluations” and “rating of standard methods”, allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers’ awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities

A Learning-based Approach to the Real-time Estimation of the Feet Ground Reaction Forces and Centres of Pressure in Humans

The feet centres of pressure (CoP) and ground reaction forces (GRF) constitute essential information in the analysis of human motion. Such variables are representative of the human dynamic behaviours, in particular when interactions with the external world are in place. Accordingly, in this paper we propose a novel approach for the real-time estimation of the human feet CoP and GRFs, using the whole-body CoP and the human body configuration. The method combines a simplified geometrical model of the whole-body CoP and a learning technique. Firstly, a statically equivalent serial chain (SESC) model which enables the whole-body CoP estimation is identified. Then, the estimated whole-body CoP and the simplified body pose information are used for the training and validation of the learning technique. The proposed feet CoP model is first validated experimentally in five subjects. Then, its real-time efficacy is assessed using dynamic data streamed on-line for one selected subject