We introduce in this paper an original planning algorithm, Zoned Artificial Repulsion (ZAR∗), designed for multiple-robot micromanipulation. The algorithm combines a modified Artificial Potential Field (APF) with A∗to efficiently compute micromanipulation trajectories. The motivation behind ZAR∗is to integrate the advantages of APF and A∗while avoiding their limitations. While APF is computationally efficient but prone to local minima, A∗guarantees completeness but has high complexity. ZAR∗employs a modified repulsive field to segment the configuration space into distinct zones, each converging to a unique local minimum. These zones are reduced to nodes in a graph, allowing A∗to compute the inter-zone transitions. The modified APF then handles navigation within the zones. This method ensures the completeness of the algorithm, avoids local minima, and significantly reduces the number of nodes in the graph, leading to a highly efficient algorithm in terms of processing time and path cost. We compare ZAR∗against A∗, APF, RRT, RRT∗, and PRM, as well as more recent hybrid algorithms. On average, ZAR∗reduces the number of nodes by 909 times and speeds up path construction efficiency (time × cost) by 499 times on average, while maintaining a 100% success rate. It also performs more than 4 times better than the best hybrid alternative of standard algorithms, making it suitable for dexterous micromanipulation tasks.

Parallel Continuum Robots (PCR) have received a lot of attention in recent years. This paper presents a new 6-degrees-of-freedom PCR derived from the conventional 3-PPSR parallel manipulator. This robot is driven by three limbs consisting of two flexible rods each and replacing the spherical and revolute joints of the original version. Each limb is mounted onto two linear axes arranged in series. To allow a direct comparison between the articulated and the continuum version, the parallel mechanism of an industrial manipulator has been replaced by an elastic structure of the same size. The simulations and the experiments show that the flexible counterpart of the manipulator is able to achieve a larger workspace, increasing the range of motion by 150% for rotations and by 157% in elevation. Moreover, the position repeatability is improved by 47% (reaching 3.4 µm) and the orientation repeatability by 57% (reaching 14.3 µrad). This can be explained by the removal of the spherical and revolute joints but also by the constant stress in the structure that acts as an anti backlash system on leadscrew actuators.

Parallel robots with configurable platforms (PRCPs) combine the benefits of parallel robots with additional functionalities such as grasping and cutting. However, some of the theoretical tools used to study classical parallel robots do not apply to parallel robots with configurable platforms. This paper uses screw theory to study the transferable wrenches from the robot's limbs to the configurable platform of an 8-DoF parallel robot. Deriving the transferable wrenches allows one to construct the screw system that is applied to each part of the configurable platform. Based on the analytical expressions of the limb and platform wrenches that have been derived and numerically validated, the mathematical tools that are used to study parallel kinematic structures, such as Grassmann line geometry, can thus be applied to the presented parallel robot with a configurable platform.

Two-fingered manipulation robotic systems are widely used in many application sand notably at small scales. The commonly employed solution is based on the attachment of a gripper on a robot. This paper introduces a new eight degrees of freedom (DoF) mechanism intended for two-fingered dexterous manipulation. The eight DoFs are obtained via a parallel architecture moved by eight actuators that are attached to the base of the robot. The novelty lies in the use of a 2-DoF configurable platform that insures two relative rotations of the gripper jaws (opening and twisting) in addition to the 6-DoF Cartesian movements. The paper presents the kinematics and the design of a proof-of-concept able to grasp, roll, translate and rotate objects in a single compact design. A prototype is operated to manipulate and insert a 2-mm screw.

Micromanipulation plays a key role in the development of complex and assembled microsystems. However, current micromanipulation solutions are often limited to small rotation amplitudes and to simple shaped objects (such as cubes). Our approach consists of developing in-hand micromanipulation techniques using dexterous microhands to manipulate arbitrary shaped objects and to perform large rotations. This paper focuses on the trajectory generation of a dexterous microhand to achieve automated repositioning by taking advantage of adhesion forces. The results on the generated trajectories show that adhesion forces can be exploited to enhance the manipulation possibilities. Moreover, experiments show that planed rotations are performed at more than 95% using an open-loop control. Dexterous micromanipulation is a promising way to perform complex manipulation tasks in microscale.

Dexterous micro-manipulation is a promising way to perform complex manipulation in micro-scale. Current dexterous micro-handling solutions are often limited to small amplitude rotations (around 90°) and to simple shaped objects (such as squares). Our approach consists in developing in-hand micro-manipulation techniques using dexterous micro-hands, taking advantage of adhesion forces, to manipulate arbitrary shaped objects. This paper focuses on the trajectory generation of a dexterous micro-hand to achieve automated repositioning. The statistical results on the generated trajectories show that the adhesion forces in micro-scale can be exploited to enhance micro-manipulation.

This paper presents preliminary results of a new robotic wrist for minimally invasive surgery. This wrist is a high-dexterity miniature robot, able to provide simultaneously the grasping/cutting (1-DoF) and rotations capabilities with 3-DoF. The grasping function is insured by the folding of the top platform of a parallel structure. The grasping capability of the wrist is part of the mechanical structure itself and can be fully controlled by external actuators. In order to validate this original approach, an experimental prototype has been fabricated using 3D printing technology at a larger scale. The inverse kinematic model has been developed and the workspace analysis was accomplished to assess the capabilities of the realized system. Finally, experimental tests have been also carried out for validating the proposed structure.

To control dynamics of a parallel robot, we should measure the state feedback accurately and fast. In this paper, we show how to estimate positions and velocities simultaneously (i.e., the state feedback) at a reasonable accuracy and speed. We did this using only the sequential visual contours of the legs. A single-iteration virtual visual servoing scheme regulates rapidly an error of these contours. We validated this theory, a step to control parallel robots at high speed by their leg kinematics, with simulations and experiments.

This paper is dedicated to vision-based modeling and control of large-dimension parallel robots driven by inextensible cables of non-negligible mass. An instantaneous inverse kinematic model devoted to vision is introduced. This model relies on the specificities of a parabolic profile hefty cable modeling and on the resulting simplified static analysis. By means of a kinematic visual servoing method, computer vision is used in the feedback loop for easier control. According to the modeling derived in this paper, measurements that allow the implementation of this visual servoing method consist of the mobile platform pose, the directions of the tangents to the cable curves at their drawing points and the cable tensions. The proposed visual servoing scheme will be applied to the control of a large parallel robot driven by eight cables. To this end, in order to obtain the aforementioned desired measurements, we plan to use a multi-camera setup together with force sensors.

Vision has often been considered as not suitable for dynamic control of robots. The experimental results presented in this paper show that it is possible to perform better with a vision based dynamic control than with a model-based control. These results were obtained using a Cartesian computed torque control fed back, without any joint sensing, by a novel Cartesian pose and velocity estimator. The latter is designed as a virtual visual servoing scheme based on sequential acquisition of sub-images and a constant acceleration motion assumption.

This paper presents a high speed visual tracking method based on non simultaneous subimages acquisition. This method is formulated as a virtual visual servoing scheme. The sequential acquisition of regions of interest has a double benefit on visual servoing. The first one is that this acquisition method allows to increase the visual control sampling frequency by reducing the data amount to acquire and to transmit by the camera. The second one is that the associated image projection model depends on the observed object pose and velocity. Thanks to this property, a new vision-based control law can be defined. The particularity of this control law is that the control output consists of the kinematic and the dynamic twists. This allows to enhance the control performance in trajectory tracking applications. The experimental results in high speed visual tracking application show the effectiveness of this approach.

This paper presents a novel method for high speed pose and velocity computation from visual sensor. The main problem in high speed vision is the bottleneck phenomenon which limits the video rate transmission. The proposed approach circles the problem out by increasing the information density instead of the data rate transmission. This strategy is based on a rotary sequential acquisition of selected regions of interest (ROI) which provides space-time data. This acquisition mode induces an image projection deformation of dynamic objects. This paper shows how to use this artifact for the simultaneous measure of both pose and velocity, at the same frequency as the ROI's acquisition one.