BACKGROUND Humans have a fantastic ability to manipulate objects of various shapes, sizes, and materials and can control the objects’ position in confined spaces with the advanced dexterity capabilities of our hands. Building machines inspired by human hands, with the functionality to autonomously pick up and manipulate objects, has always been an essential component of robotics. The first robot manipulators date back to the 1960s and are some of the first robotic devices ever constructed. In these early days, robotic manipulation consisted of carefully prescribed movement sequences that a robot would execute with no ability to adapt to a changing environment. As time passed, robots gradually gained the ability to automatically generate movement sequences, drawing on artificial intelligence and automated reasoning. Robots would stack boxes according to size, weight, and so forth, extending beyond geometric reasoning. This task also required robots to handle errors and uncertainty in sensing at run time, given that the slightest imprecision in the position and orientation of stacked boxes might cause the entire tower to topple. Methods from control theory also became instrumental for enabling robots to comply with the environment’s natural uncertainty by empowering them to adapt exerted forces upon contact. The ability to stably vary forces upon contact expanded robots’ manipulation repertoire to more-complex tasks, such as inserting pegs in holes or hammering. However, none of these actions truly demonstrated fine or in-hand manipulation capabilities, and they were commonly performed using simple two-fingered grippers. To enable multipurpose fine manipulation, roboticists focused their efforts on designing humanlike hands capable of using tools. Wielding a tool in-hand became a problem of its own, and a variety of advanced algorithms were developed to facilitate stable holding of objects and provide optimality guarantees. Because optimality was difficult to achieve in a stochastic environment, from the 1990s onward researchers aimed to increase the robustness of object manipulation at all levels. These efforts initiated the design of sensors and hardware for improved control of hand–object contacts. Studies that followed were focused on robust perception for coping with object occlusion and noisy measurements, as well as on adaptive control approaches to infer an object’s physical properties, so as to handle objects whose properties are unknown or change as a result of manipulation. ADVANCES Roboticists are still working to develop robots capable of sorting and packaging objects, chopping vegetables, and folding clothes in unstructured and dynamic environments. Robots used for modern manufacturing have accomplished some of these tasks in structured settings that still require fences between the robots and human operators to ensure safety. Ideally, robots should be able to work side by side with humans, offering their strength to carry heavy loads while presenting no danger. Over the past decade, robots have gained new levels of dexterity. This enhancement is due to breakthroughs in mechanics with sensors for perceiving touch along a robot’s body and new mechanics for soft actuation to offer natural compliance. Most notably, this development leverages the immense progress in machine learning to encapsulate models of uncertainty and support further advances in adaptive and robust control. Learning to manipulate in real-world settings is costly in terms of both time and hardware. To further elaborate on data-driven methods but avoid generating examples with real, physical systems, many researchers use simulation environments. Still, grasping and dexterous manipulation require a level of reality that existing simulators are not yet able to deliver—for example, in the case of modeling contacts for soft and deformable objects. Two roads are hence pursued: The first draws inspiration from the way humans acquire interaction skills and prompts robots to learn skills from observing humans performing complex manipulation. This allows robots to acquire manipulation capabilities in only a few trials. However, generalizing the acquired knowledge to apply to actions that differ from those previously demonstrated remains difficult. The second road constructs databases of real object manipulation, with the goal to better inform the simulators and generate examples that are as realistic as possible. Yet achieving realistic simulation of friction, material deformation, and other physical properties may not be possible anytime soon, and real experimental evaluation will be unavoidable for learning to manipulate highly deformable objects. OUTLOOK Despite many years of software and hardware development, achieving dexterous manipulation capabilities in robots remains an open problem—albeit an interesting one, given that it necessitates improved understanding of human grasping and manipulation techniques. We build robots to automate tasks but also to provide tools for humans to easily perform repetitive and dangerous tasks while avoiding harm. Achieving robust and flexible collaboration between humans and robots is hence the next major challenge. Fences that currently separate humans from robots will gradually disappear, and robots will start manipulating objects jointly with humans. To achieve this objective, robots must become smooth and trustable partners that interpret humans’ intentions and respond accordingly. Furthermore, robots must acquire a better understanding of how humans interact and must attain real-time adaptation capabilities. There is also a need to develop robots that are safe by design, with an emphasis on soft and lightweight structures as well as control and planning methodologies based on multisensory feedback.

Trends and challenges in robot manipulation

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Using Algebraic Geometry

IEEE International Conference on Robotics and Automation

This study presents a dynamic real-time detection method for surface deformation and full field strain in recycled aggregate concrete-filled steel tubular columns (RACSTCs). Automatic calibration and dynamic surface tracking measurement are utilized and mathematical models combining the four-ocular visual coordinates and point cloud matching are proposed. The 3D deformation of the RACSTCs under low cyclic loading are gathered every 60 s as sample images. The four-ocular vision system constitutes two groups of binocular stereoscopic vision systems. The 3D deformation surface is reconstructed by multi-ocular vision coordinate association, image preprocessing, and point cloud registration. The measurement results are validated by comparison against laser range finder data. The standard deviation mean value of 10 specimens measured by the proposed method relative to the true value is 1.23%; the mean error of the maximum absolute value is 2.82%.

Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision

Locking devices are widely used in robotics, for instance to lock springs and joints or to reconfigure robots. This review article classifies the locking devices currently described in the literature and performs a comparative study. Designers can therefore better determine which locking device best matches the needs of their application. The locking devices are divided into three main categories based on different locking principles: 1) mechanical locking, 2) friction-based locking, and 3) singularity locking. Different locking devices in each category can be passive or active. Based on an extensive literature survey, this article summarizes the findings by comparing different locking devices on a set of properties of an ideal locking device.

https://biblio.vub.ac.be/vubirfiles/26793095/201504_lockingdevices.pdf

Lock Your Robot: A Review of Locking Devices in Robotics

A new class of reconfigurable parallel kinematic machines

This paper presents the design of a reconfigurable 3-DoF parallel kinematics manipulator. The main feature of the device is the ability to change the mobility of its moving platform from pure translation to pure rotation. The manipulator kinematics is conceived so that, when a particular configuration of the manipulator is reached, the transition between the two working modes is possible by changing the configuration of a metamorphic universal joint, which is used to connect the legs of the manipulator with the moving platform. The mechanical design of the joint, which is in fact a lockable spherical joint, is illustrated. With the joint integrated into the robot architecture, an instantaneous overconstrained kinematics is exploited to manage the phase of reconfiguration of the whole mechatronic device. A kineto-static analysis provides information about the influence of geometric parameters on its functional design. The manipulator shows simple kinematics and statics models, as well as good kinematic and static performances. Eventually, the versatility of the manipulator is shown by proposing some advanced manufacturing applications in which it could find use.

Analysis and Design of a Reconfigurable 3-DoF Parallel Manipulator for Multimodal Tasks

In the design optimization of a robot the configuration-dependent modal analysis can be a powerful tool to be exploited when high stiffness and high dynamic performances are concurrently required. In this paper the elastodynamics of a lower-mobility Parallel Kinematic Machine for pure translational motions is analyzed. The vibrational modes and the natural frequencies of the robot are evaluated as functions of the end effector position inside the workspace. A finite element model including kinematic joints is used to perform a series of modal analyses in a grid of points inside the workspace. A polynomial regression gives continuous volume maps of the natural frequencies distributions. The numerical model is validated by comparison with experiments: a modal analysis is conducted on a set of inertance Frequency Response Functions acquired on several points of the machine components as a result of an excitation given by an instrumented hammer. A Natural Frequency Difference analysis validates the model under certain conditions and highlights some critical issues to be focused on in future works.

Configuration-dependent modal analysis of a Cartesian parallel kinematics manipulator: numerical modeling and experimental validation

The paper proposes the mechanical design of a lockable spherical joint, which is designed to be manually or automatically configured in different kinematic solutions. The device is conceived for being used as a conventional spherical joint or converted in a universal joint, or still downgraded to a revolute pair. Therefore different configurations can be chosen according to user needs. In particular, two of the three axes of revolution, arranged in the typical roll-pitch-roll sequence of robot spherical wrists, can be locked alternately in order to provide two differently arranged universal joints. It can be demonstrated that such behavior allows to activate different mobilities of a class of reconfigurable parallel kinematics manipulators and for this task the device has been dimensioned. The transition between such mobilities occurs exploiting the concept of over-constrained kinematics, which is realized by the lockable joint during the switching phase in order to avoid an instantaneous mobility of the robot.

Details on the Design of a Lockable Spherical Joint for Robotic Applications

During the last few decades, robotic grippers are developed by research community to solve grasping complexities of several objects as their primary objective. Due to the increasing demands of industries, many issues are rising and remain unsolved such as in-hand manipulation, placing object with appropriate posture. Operations like twisting, altering orientation of object, in a hand, requires significant dexterity of the gripper that must be achieved from a compact mechanical design at the first place. In this paper, a newly designed gripper is proposed whose primary goal is to solve in-hand manipulation. The gripper is derived from four identical finger modules; each module has four DOFs, consists of a five bar linkage to grasp and additional two DOFs mechanism similar to ball spline is conceived to manipulate object. Hence the easily constructible gripper is capable to grasp and manipulate plurality of object. As a preliminary inspection, an optimized platform that represents the central concept of the proposed gripper is developed, to the aim of evaluating kinematic feasibilities of manipulation of different objects.

Luca Carbonari

Papers

A new class of reconfigurable parallel kinematic machines

Analysis and Design of a Reconfigurable 3-DoF Parallel Manipulator for Multimodal Tasks

Configuration-dependent modal analysis of a Cartesian parallel kinematics manipulator: numerical modeling and experimental validation

Details on the Design of a Lockable Spherical Joint for Robotic Applications

A dexterous gripper for in-hand manipulation