Since the birth of the first industrial robot in the early 1960s, robotics has often replaced humans for tedious and repetitive tasks in the industrial world. To meet these challenges, industrial robots have needed to become specialized. They have been designed according to the task that needs to be performed. In the early 1980s, the ambition to equip robots with robotic hands with universal capabilities led to the development of robotic grasping research. The emergence of more agile industry and also collaborative robotics requires the development of new generation grippers: more versatile, with not only adaptive grasping capabilities but also dexterous manipulation capabilities. The development of flexible multi-fingered grippers with both adaptive grasping and in-hand manipulation capabilities remains a complex issue for human-like dexterous manipulation. After four decades of research in dexterous manipulation, many robotic hands have been developed. The development of these hands however remains a key challenge, as the dexterity of robot hands is far from human capabilities. The aim of this monograph is, through the evolution of robotics from industrial and manufacturing robotics to service and collaborative robotics, to show the evolution of the grasping function. From industrial grippers to dexterous robot hands, and the stakes inherent today to new robotic applications in open, dynamic environments where humans are likely to evolve.

Structural bonding is a technique increasingly used in the industrial field. For applications in aggressive environments such as seawater, predicting the effect of moisture on the mechanical behavior of bonded assemblies is of paramount importance. The objective of this work is to analyze water diffusion in an epoxy adhesive material and, more specifically, to propose a robust method for choosing the most appropriate diffusion model. Experimental studies of the water absorption in a two-component epoxy structural adhesive, using gravimetry and X-ray tomography, were first performed. The presence of a population of pore-type defects in the polymeric joint helped to characterize the evolution of water diffusion kinetics. Thus, two diffusion mechanisms were identified: a first one related to the migration of water molecules within the adhesive matrix, and a second one related to the penetration of water into the pores. Then, Dual-Fick and Langmuir models were retained, as the two diffusion models most likely to capture the above mechanisms. Although it was shown that both models could give similar results in terms of global diffusion behavior, the results arising from these two models differ at the local scale, especially for extended periods of time. Therefore, special attention was paid to the second absorption mechanism, and a comparison of waterfronts between theoretical predictions and experimental tomographic data was achieved, leading to the final choice of a Dual-Fick diffusion model.

In the present study, the crack propagation behavior under cyclic loading of an epoxy-based adhesive was investigated by the means of an Arcan device, in the mixed-mode plane I+II. The strain energy release rate (SERR) was calculated through an inverse identification method using a Finite Element (FE) modeling. The evolution of the cracked surfaces revealed three main stages during the tests. It was found that under fatigue loading, the crack propagated at a value of SERR per cycle lower than the values of the critical SERR calculated under monotonic loads. The evolutions of the crack propagation rate against the SERR are also provided to highlight the effects of the mixed-mode ratio on fatigue crack propagation.

Work-related musculoskeletal disorders (WMSDs) in industry represent a major health issue worldwide. Collaborative robotics, in which a human and a robot collaborate together to jointly carry out a task, is a possible solution to help decrease the prevalence of WMSDs. But designing efficient collaborative robots requires to assess the ergonomic benefit they offer. Similar to other domains such as vehicle or workstation design, the use of a digital human simulation (DHS) can cut down the development cost and time of a collaborative robot by replacing the physical mock-up of the robot with a virtual one easier to modify. Simulating human–robot collaborative tasks however pose specific challenges because the human and the robot form a highly coupled dynamic system in which the motion of each partner depends on the forces exchanged. Therefore, a dynamic simulation is required to obtain reliable measurements for ergonomic assessments. The first part of this chapter details the challenges of DHS for collaborative robotics. State-of-the-art work on DHS including collaborative robots is reviewed to identify which questions currently remain open. An optimization-based method is then proposed to animate a digital human model (DHM) in the context of human–robot collaboration. The second part of this chapter presents an application of the proposed DHM animation method. A human–robot collaborative task is successfully simulated and allows to quantify the effect of kinematic, dynamic, and control parameters of the robot on the DHM posture and effort.

Enhancing the performance of technical movements aims both at improving operational results and at reducing biomechanical demands. Advances in human biomechanics and modeling tools allow to evaluate human performance with more and more details. Finding the right modifications to improve the performance is, however, still addressed with extensive time consuming trial-and-error processes. This paper presents a framework for easily assessing human movements and automatically providing recommendations to improve their performances. An optimization-based whole-body controller is used to dynamically replay human movements from motion capture data, to evaluate existing movements. Automatic digital human simulations are then run to estimate performance indicators when the movement is performed in many different ways. Sensitivity indices are thereby computed to quantify the influence of postural parameters on the performance. Based on the results of the sensitivity analysis, recommendations for posture improvement are provided. The method is successfully validated on a drilling activity.

The control of complex dynamic systems, both in their behaviour and in their mission, goes through the implementation of multi-loop control architectures based on information about the system internal state and from the environment, as well as on the mission plan state. This results in systems that are becoming increasingly autonomous, for which requirements in terms of safety and reliability, as well as expected performance, are increasingly high. Research works developed at ONERA in the field of control for autonomous systems cover all levels of the control architectures, which are basically structured with respect to temporal aspects, as well as the level of abstraction that they entail for the system dynamic.

This paper presents a method for the human ankle joint kinematic measurement by using a mechanical linkage having 6 d.o.f., all equipped with position sensors. The device allows a complete identification of all kinematic parameters of the joint which can be expressed here in the form of the Instantaneous Helical Axis and gait portraits for 3 principal rotations of the joint: dorsi/planta flexion, pronation/supination and internal/external rotation. A symmetry study of the 2 ankles during walking was also realized which highlighted the asymmetrical nature of the human gait as the FFT analysis gives a significant difference in amplitude between the rotational velocity vectors of the 2 ankle joints.

Movement variability is an essential characteristic of human movement. It occurs in all kinds of activity including work-place tasks. However it is almost ignored in workstation design, where expected movements are highly standardized for productivity and quality considerations. Neglecting this variability may lead designers to omit parts of the future operator’s movements, thus leading to incomplete assessment of biomechanical risk factors. This article describes a model-based virtual human controller intended to simulate the movement variability induced by muscle fatigue during a repetitive activity. It is built using a multibody dynamics framework and a 3-compartments muscle fatigue model. The simulation of a repetitive pointing activity is described. Our demonstrator reproduces some of the adaptive behaviors described in the literature. This demonstrator must still be validated by experimental human data, but it opens interesting perspectives for DHM software improvements and more reliable ergonomic assessments from the early stages of workstation design.

This paper proposes a method for the determination of the kinematics of the human ankle joint using a 6 d.o.f. electro-goniometer. The mechanical design and the kinematic model of the device are presented. The authors performed the Instantaneous Helical Axis (IHA) measurement during walking experiment and proposed a kinematics descriptor for the identification of gait phases and sub-phases, based on the measure of the direction of the IHA and the magnitude of the rotational vector. First results reveal manifest potential and perspective of development of this method, as good repeatability is obtained for the measurement of the kinematics descriptor during different gait cycles.

Movement variability is an essential characteristic of human movement. However, despite its prevalence, it is almost completely ignored in workstation design. Neglecting this variability can lead to skip over parts of the future operator's movements, thus bring to incomplete assessment of biomechanical risk factors. This paper starts with a focus on movement variability in occupational activities. Then, as an example of feasibility, it describes a Digital Human Model framework intended to simulate the movement variability induced by muscle fatigue. The demonstrator is based on several simulation environments, namely (1) XDE, a virtual human simulation software tool previously used for ergonomics analyses, (2) a dynamic three-compartment model of muscle fatigue and recovery, and (3) OpenSim, a dynamic musculoskeletal simulation software. The demonstrator is a first step towards tools to assist designers in considering movement variability for improved ergonomics at the workstation.

This paper presents a design process based on an advanced flexible robots modeling tool associated with realistic actuators models and pre-defined control architecture. This process implements dedicated feasibility and performance indicators, which are used to evaluate a design and its sensitivity on the considered parameters. The proposed approach is illustrated with theoretical and experimental results obtained with the YAKA robot.

Simulations of human walking is a current subject of research in different areas. Between the different approaches for the synthesis of human walking (Xiang et al. 2010), two clearly distinguish: d...

Input shaping techniques for vibration control of flexible structures received many interest due to its simplicity and efficiency proven in various practical cases. This paper proposes an adaptation of those techniques by addressing two main specificities of robot manipulators. As a first contribution, the design of input shapers for multiple-input and multiple-modes complex robot is studied. Design indicators based on elastic-dynamic model are proposed in order to choose one input shaper design among thousands of possible combinations. As a second contribution, the vibration mode frequency evolution of robot manipulators over their workspace is compensated by an adaptive control scheme. It is proposed to interpolate the modal variations over the workspace and tune the input shaper parameters dynamically. This contribution is implemented on a large scale flexible robot and shows promising results.

Collaborative robotics is a possible solution to the problem of musculoskeletal disorders (MSDs) in industry, but efficiently designing such robots remains an issue because ergonomic assessment tools are ill-adapted to such devices. This paper presents a generic method for performing detailed ergonomic assessments of co-manipulation activities and its application to the optimal design of collaborative robots. Multiple ergonomic indicators are defined to estimate the different biomechanical demands which occur during manual activities. For any given activity, these indicators are measured through dynamic virtual human simulations, for varying human and robot features. Sensitivity indices are thereby computed to quantify the influence of each parameter of the robot and identify those which should mainly be modified to enhance the ergonomic performance. The sensitivity analysis also allows to extract the indicators which best summarize the overall ergonomic performance of the activity. An evolutionary algorithm is then used to optimize the influential parameters of the robot with respect to the most informative ergonomic indicators, in order to generate an efficient robot design. The whole method is applied to the optimization of a robot morphology for assisting a drilling activity. The performances of the resulting robots confirm the relevance of the proposed approach.

The control of complex dynamic systems, both in their behavior and in their mission, goes through the implementation of multi-loop control architectures based on information about the system internal state and from the environment, as well as on the mission plan state. This results in systems that are becoming increasingly autonomous, for which requirements in terms of safety and reliability, as well as expected performance, are increasingly high. Research works developed at ONERA in the field of control for autonomous systems cover all levels of the control architectures, which are basically structured with respect to temporal aspects, as well as the level of abstraction that they entail for the system dynamic. We will consider them in this paper by increasing level. We will discuss the advances achieved recently in the robust control techniques of uncertain dynamic systems generally implemented at the lower control level and we will discuss their extensions to consider input and output constraints, as well as the hybrid nature of most of the systems considered. To design task level control primitives that take place just above the previous control loops, we will introduce sensor-based robust and non-linear control techniques. These are based on information on the environment extracted from exteroceptive sensors, to adapt system behavior to uncertainties and perturbations. Multi-sensor and/or multi-objective controls will be discussed in this particular context. We will also present several recent results in the field of trajectory tracking based on visual navigation techniques in complex environments, which combine objectives and constraints within the same control architecture. We will discuss how model predictive control (MPC) techniques and advanced optimization techniques can be used for solving the resulting control problems. In addition, we will discuss several ongoing developments of these methods by exploiting distributed model predictive control techniques (DMPC) and predictive control of hybrid systems. Finally, integration with the control architectures at the upper level of reactive, predictive and distributed planning capabilities will be proposed to accommodate time constraints and uncertainties in decision.

In this paper, we propose physically meaningful energy related safety indicators for robots sharing their workspace with humans. Based on these indicators, safety criteria are introduced as constraints in the control algorithm. The first constraint is placed on the kinetic energy of the robotic system to limit the amount of dissipated energy in case of collision. This constraint depends on the distance between the robot and the human operator. The distance is computed with a point cloud based algorithm acquired using a set of depth sensors (Kinects). The second constraint is on the amount of potential energy that is allowed to be generated within the human-robot system during physical contact. It is used to modulate the contact forces. The control algorithm is formulated as an optimization problem and computes every time step the actuation torques for a KUKA LWR4 manipulator given some task to be performed, the introduced constraints and the physical limitations of the system to respect. The overall framework allows a human operator to safely enter the robot's workspace and physically interact with it.

Medical orthoses aim at guiding anatomical joints along their natural trajectories while preventing pathological movements, especially in case of trauma or injuries. The motions that take place between bone surfaces have complex kinematics. These so-called arthrokinematic motions exhibit axes that move both in translation and rotation. Traditionally, orthoses are carefully adjusted and positioned such that their kinematics approximate the arthrokinematic movements as closely as possible in order to protect the joint. Adjustment procedures are typically long and tedious. We suggest in this paper another approach. We propose mechanisms having intrinsic self-aligning properties. They are designed such that their main axis self-adjusts with respect to the joint's physiological axis during motion. When connected to a limb, their movement becomes homokinetic and they have the property of automatically minimizing internal stresses. The study is performed here in the planar case focusing on the most important component of the arthrokinematic motions of a knee joint.

Balance is an essential feature of humanoids but, despite a strong understanding of its laws and dynamics, it remains an open problem for control applications. Optimization-based control approaches explicitly include balance dynamics and constraints in the control problem in order to capture at best the behavior of the system and fully exploit it to reach complex control objectives. Although theoretically appealing, these approaches intrinsically induce a significant computational burden. In practice, this implies to resort to simplifications on the model and problem complexities, which limits the capacity to actually generate complex behaviors. In this chapter, an overview of the balance problem is first proposed. A general, abstract formulation of the balance control problem as an optimal control one is then derived. Three major approaches can be found in the literature, coping with the computational complexity of the general balance optimization problem. They range from offline motion planning to reactive whole-body control and are presented in the remainder of the chapter.

Enhancing the performances of technical gestures is a great concern for human beings, and aims both at improving the operational results and at reducing the associated biomechanical demands. Thanks to the advances in human biomechanics and modeling tools, human performances can be evaluated with more and more details. However, finding the right modifications which improve such performances is still addressed with extensive time-consuming trial and error processes. This paper presents a method for automatically providing recommendations to improve human gestures. An optimization-based whole-body controller is used to dynamically replay human gestures from motion capture data, in order to acquire and evaluate the initial gesture. Virtual human simulations are then run to estimate performance indicators, when the gesture is performed in many different ways, in order to compute sensitivity indices for quantifying the influence of the gesture parameters on the performances. Based on this sensitivity analysis, recommendations for gesture improvement are provided. The whole method is validated on a drilling gesture. The consistency of the replayed motion and the significant increase in the performances of the gesture modified according to the provided recommendations confirm the relevance of the proposed approach.