Cellular-enabled collaborative robots are becoming paramount in Search-and-Rescue (SAR) and emergency response. Crucially dependent on resilient mobile network connectivity, they serve as invaluable assets for tasks like rapid victim localization and the exploration of hazardous, otherwise unreachable areas. However, their reliance on battery power and the need for persistent, low-latency communication limit operational time and mobility. To address this, and considering the evolving capabilities of 5G/6G networks, we propose a novel SAR framework that includes Mission Planning and Mission Execution phases and that optimizes robot deployment. By considering parameters such as the exploration area size, terrain elevation, robot fleet size, communication-influenced energy profiles, desired exploration rate, and target response time, our framework determines the minimum number of robots required and their optimal paths to ensure effective coverage and timely data backhaul over mobile networks. Our results demonstrate the trade-offs between number of robots, explored area, and response time for wheeled and quadruped robots. Further, we quantify the impact of terrain elevation data on mission time and energy consumption, showing the benefits of incorporating real-world environmental factors that might also affect mobile signal propagation and connectivity into SAR planning. This framework provides critical insights for leveraging next-generation mobile networks to enhance autonomous SAR operations.

Myocontrolled robotic hands require accurate and responsive control to regulate grasp strength effectively. However, many human-in-the-loop (HITL) control systems still lack robust closed-loop solutions for fine grip force regulation, limiting their performance. This paper presents a novel control system for myocontrolled hands that combines contact force sensing and vibrotactile feedback to enable more natural and precise grasp interaction. The system features an advanced force controller based on fuzzy logic, with parameter optimization guided by user preferences collected through a graphical user interface (GUI) using Global Learning of Input–Output Strategies from Pairwise Preferences (GLISp). It is compared against heuristic model and neural network based controllers. The system was validated through real-world experiments using the AR10 robotic hand with OptoForce fingertip sensors, demonstrating improved adaptability and fine force regulation capabilities for the user.

The aim of this work is the development of a complete software package to exploit a robotic anthropomorphic hand, such as the Allegro Robotic Hand, allowing to easily perform accurate grasps. It includes the Inverse and the Forward Kinematics and allows storing different configurations as well as to perform and store predefined grasps while controlling the movement speed. The software presented in this document allows the easy use of the Allegro Robotic Hand, enhancing its capabilities and allowing further research on grasp theory by facilitating the experimental implementation. The software is designed to work with all the Allegro Hand versions despite their differences. It is an improved alternative to the manufacturer's version, offering a powerful framework for grasping. Besides, it has been designed to easily adapt to other robotic anthropomorphic hands.

This paper introduces a novel strategy to enhance the dexterous in-hand manipulation capabilities of robotic hands, focusing on reorienting unknown objects around any specified axis. The proposed method leverages tactile sensing and sensor-to-motor mapping to achieve precise and adaptive manipulation without prior object knowledge. The strategy employs circular finger movements to maintain stable and secure grasps, ensuring even pressure distribution. Preliminary experiments conducted with the Allegro robotic hand validate the efficacy of the approach across various orientations, demonstrating its potential for practical applications.

Traffic management of multi-AGV fleets through improved dynamic resource reservation (IDRR) has been recently proven to be an interesting alternative within zone control-based approaches in terms of time completion efficiency in in-house transportation tasks. In this paper, preliminary results show that the combination of IDRR with PRIM allocation, a classical market-based task allocation strategy, yields a significant reduction of finishing times in a standard benchmark problem. Hence, further improvements are to be expected from deeper suitability analyses plus better tailored adaptations of optimal task allocation algorithms to zone control-based fleet management systems, and in particular to IDRR.

This paper presents the implementation of grasp quality measures in a real-world robotic setup. Specifically, the largest wrench that a grasp can resist due to perturbations in any direction has been computed considering both the actual contact points and the magnitude of the actual grasping forces. The experiments were done using the Allegro Hand with OptoForce tactile sensors and a Fastrak motion tracking system. In this work, the grasp quality measure was experimentally computed using different objects without needing their detailed geometric models, and numerical results are provided.

Mission-critical operations, particularly in the context of Search-and-Rescue (SAR) and emergency response situations, demand optimal performance and efficiency from every component involved to maximize the success probability of such operations. In these settings, cellular-enabled collaborative robotic systems have emerged as invaluable assets, assisting first responders in several tasks, ranging from victim localization to hazardous area exploration. However, a critical limitation in the deployment of cellular-enabled collaborative robots in SAR missions is their energy budget, primarily supplied by batteries, which directly impacts their task execution and mobility. This paper tackles this problem, and proposes a search-and-rescue framework for cellular-enabled collaborative robots use cases that, taking as input the area size to be explored, the robots fleet size, their energy profile, exploration rate required and target response time, finds the minimum number of robots able to meet the SAR mission goals and the path they should follow to explore the area. Our results, i) show that first responders can rely on a SAR cellular-enabled robotics framework when planning mission-critical operations to take informed decisions with limited resources, and, ii) illustrate the number of robots versus explored area and response time trade-off depending on the type of robot: wheeled vs quadruped.

We introduce a simple yet effective approach for dense depth reconstruction that operates directly on raw disparity data, eliminating the need for additional disparity refinement stages. By leveraging disparity maps generated from conventional stereo methods, we train a U-Net-based model to directly map disparity to depth, bypassing complex feature engineering. Our method capitalizes on the robustness of traditional stereo matching techniques to varying scenes, focusing exclusively on dense depth reconstruction. This approach not only simplifies the training process but also significantly reduces the requirement for large-scale training datasets. Extensive evaluations demonstrate that our method surpasses classical stereo matching frameworks and state-of-the-art classical post-refinement techniques, achieving superior accuracy. Additionally, our approach offers competitive inference times, comparable to classical as well as end-to-end deep learning methods, making it highly suitable for real-time robotic applications.

Mobile manipulators working in semi-structured human environments need smart vision capabilities to perceive the world and interact with human operators. With this purpose in mind, this paper presents the development of a robotic head module composed of an OAK-D camera mounted on a pan-and-tilt WidowX XM430 structure. The OAK-D camera provides high-resolution images, including stereo vision and depth sensing, and advanced capabilities based on embedded artificial intelligence functions. These features combined with the high-range motion of the WidowX XM430, allow the head module to have advanced visual tracking capabilities. The implementation has been done using ROS (Robot Operating System), which allows the head module to be easily integrated to any mobile manipulator.

Autonomous service robots are conceived to work in semi-structured and complex human environments performing a wide range of tasks and, hence, one of their main challenges is to be able to adapt the stages of the perceive-plan-execute cycle to perturbations ranging from small deviations on the poses of objects to large unexpected changes in the environment, as well as to recover from potential failures. To advance in this direction, this paper proposes an ontology-based manipulation framework where reasoning is used to enhance perception with situation awareness, planning with domain awareness and execution with the awareness of the execution structures. The combination of these different types of awareness allows the robot to have different adaptation capabilities. The conceptual schema of the framework is presented and discussed and the main future implementation challenges are pointed out.

In this article, the variety of approaches proposed in the literature to address the problem of mapping human to robot hand motions are summarized and discussed. We particularly attempt to organize under macrocategories the great quantity of presented methods that are often difficult to be seen from a general point of view due to different fields of application, specific use of algorithms, terminology, and declared goals of the mappings. First, a brief historical overview is reported, in order to provide a look on the emergence of the human to robot hand mapping problem as a both conceptual and analytical challenge that is still open nowadays. Thereafter, the survey mainly focuses on a classification of modern mapping methods under the following six categories: direct joint, direct Cartesian, task-oriented, dimensionality reduction based, pose recognition based, and hybrid mappings. For each of these categories, the general view that associates the related reported studies is provided, and representative references are highlighted. Finally, a concluding discussion along with the authors' point of view regarding future desirable trends are reported.

The article deals with the problem of planning in the task space in the presence of vector fields, while verifying and validating the constraints in the configuration space. The proposed approach, called the Task Space Vector Field Rapidly-exploring Random Tree (TSVF-RRT) algorithm, extends the Task-Space Rapidly-exploring Random Trees (TS-RRT) algorithm by incorporating vector fields into the task space, while avoiding non-trivial constraints on the configuration space. The planner restricts the search to a lower dimensional space, minimizing the upstream functional. To evaluate the proposed approach, graphical simulations are presented, carried out using a planar manipulator of 10 DOF. Possible advantages that encourage further research in this line are discussed.

This paper describes a procedure to generate valid robot trajectories for a given sequence of points defining a geometric path, which is a quite frequent output of many robot motion planners. The proposed approach takes into account physical constraints of the robot, as the maximum velocity and the maximum acceleration that each joint can reach, and has been implemented in C++ as a general tool that can be used with different robots.

The paper presents a tool to graphically display the contact forces applied by the fingers of a robotic hand when doing grasping and in-hand manipulation. The forces are computed in two ways, on one side, directly using the measurements of tactile forces in the fingertips and, on the other, using the torques applied by the motors in the finger joints. The implemented tool also allows to command and move the real robotic hand by specifying the complete hand configuration or any single joint, and see graphically the hand simulation. Real results are shown using the Allegro hand with tactile sensors WTS-FT.

Industry 4.0 demands interconnected production lines that consist of modular assets. Recent advances of wireless communication technologies allow a large connectivity of devices and approach the performance of wireline communication, specifically regarding throughput, latency and reliability. As a result, more and more time critical connections can be performed wirelessly. Both attributes foster the emergence of edge computing, a concept that can efficiently utilize distributed computational resources. This is particularly beneficial for modular assets that have limited energy supply and capacity of computation hardware. Autonomous mobile robots offer high potential for object transportation, inspection and manipulation in shared workspaces with human operators. With edge computing, heavy computations can then be offloaded to more powerful computers or edge data centers to speed up the decision-making process and increase the productivity. For an efficient orchestration strategy of computation and communication resources, various task requirements in terms of latency, bandwidth, cost and energy must be considered. To this end, we aim at evaluating the requirements in autonomous and collaborative assembly lines, a use case that comprises diverse tasks including latency-sensitive ones in dynamic, uncertain, multi-agent environments. This work focuses on discussing latency requirements on the basis of a collaborative safety mode and autonomous robotic insertion.

This paper presents a simple strategy to allow the rotation of unknown objects using a robotic hand equipped with tactile sensors. The tactile and kinematic information, obtained while that the object is manipulated, is used to determine the hand configurations that change the object position. The approach was successfully tested using an anthropomorphic robotic hand (Allegro Hand), with the fingertips modified to include tactile sensors (WTS-FT). Using three fingers of the hand, every-day objects were successfully rotated without using their model for the determination of the hand movements.

This paper presents a planning procedure that allows an anthropomorphic dual-arm robotic system to perform a manipulation task in a natural human-like way by using demonstrated human movements. The key idea of the proposal is to convert the demonstrated trajectories into attractive potential fields defined over the configuration space and then use an RRT*-based planning algorithm that minimizes a path-cost function designed to bias the tree growth toward the human-demonstrated configurations. This paper presents a description of the proposed approach as well as results from a conceptual and a real application example, the latter using a real anthropomorphic dual-arm robotic system. A path-quality measure, based on first-order synergies (correlations between joint velocities) obtained from real human movements, is also proposed and used for evaluation and comparison purposes. The obtained results show that the paths obtained with the proposed procedure are more human-like.

This paper addresses the problem of obtaining human-like motions on hand-arm robotic systems performing grasping actions. The focus is set on the coordinated movements of the robotic arm and the anthropomorphic mechanical hand, with which the arm is equipped. For this, human movements performing different grasps are captured and mapped to the robot in order to compute the human hand synergies. These synergies are used to both obtain human-like movements and to reduce the complexity of the planning phase by reducing the dimension of the search space. In addition, the paper proposes a sampling-based planner, which guides the motion planning following the synergies and considering different types of grasps. The introduced approach is tested in an application example and thoroughly compared with a state-of-the-art planning algorithm, obtaining better results.

Many of the solutions proposed for the object manipulation problem are based on the knowledge of the object features. The approach proposed in this paper intends to provide a simple geometrical approach to securely manipulate an unknown object based only on tactile and kinematic information. The tactile and kinematic data obtained during the manipulation is used to recognize the object shape (at least the local object curvature), allowing to improve the grasping forces when this information is added to the manipulation strategy. The approach has been fully implemented and tested using the Schunk Dexterous Hand (SDH2). Experimental results are shown to illustrate the efficiency of the approach.

This paper presents a method to compute contact forces for bimanual grasps. The method is based on the optimization of the force distribution of the hands and minimizing the force exerted by each finger, using two different cost functions. Both cost functions and the constrains of the optimization problem are formulated as functions of the joint torques based on the existing relation between the grasping forces, the hand-jacobian matrix and the torque of the joint fingers. Additionally, a bimanual grasp index is presented to measure the force distribution between the hands. The paper includes some application examples of the proposed approach.

This paper discusses motion planning for anthropomorphic dual-arm robots. It introduces a measure of the similarity of the movements needed to solve two given tasks. Planning using this measure to select proper arm synergies for a given task improves the planning performance and the resulting plan.

This paper presents a framework schema that allows to bring simulation results obtained from different dual-arm robotic applications to executions in real environments. The framework uses the Robot Operating System (ROS) to communicate each component of the dual-arm, and the robotic software tool The Kautham Project for the computation of motion paths, inverse kinematics validations, collision checking and the graphical visualization of the simulated environment. The Anthropomorphic Dual Arm Robotic System (ADARS) was used in this work which is composed of two robotic arms of 6 degree of freedom, two anthropomorphic hands, and tactile sensors in the fingertips. The real execution of two different applications are presented to show the robustness of the framework. The resulting framework is general enough, allowing the reimplementation with minimum changes in any other dual-arm system.

This paper addresses the problem of grasp synthesis with force-closure for 3D articulated objects consisting of n links and considering frictional and non-frictional contacts. The surface of each link is represented by a finite set of points. First, the article presents a methodology to represent the generalized wrench space for an articulated 3D object with n links. This wrench space is generated by the forces applied on the links of the articulated object. Second, the algorithm that finds the set of points which allow a force-closure grasp using the generalized wrench space is described. The approach has been implemented and some illustrative examples are included in the paper.

This paper deals with the problem of combining motion and task assignment for a dual-arm robotic system. Each arm of the system performs independent tasks in a cluttered environment. Robot actions are determined to remove potential obstacles and obtain collision-free paths to grasp the target objects. The approach uses the information provided by the motion planner to build a graph structure in order to represent the obstacles to be removed. The graph is used, first, to decide which is the next motion path to be computed, and, second, to assign the tasks to each arm of the robotic system. The approach has been implemented for a dual-arm robotic system and real experiments are presented in the paper.

This paper presents an algorithm to compute precision grasps for bulky objects using two anthropomorphic hands. We use objects modeled as point clouds obtained from a sensor camera or from a CAD model. We process the point clouds dividing them into two set of slices, one for each hand, where we look for sets of triplets of points. Each triplet must accomplish some physical conditions based on the structure of the hands. Then, triplets of points from each set of slices are evaluated to find a combination that satisfies the force closure condition (FC). Once one valid couple of triplets have been found the inverse kinematics of the system is computed in order to know if the points are kinematically reachable by the hands, if so, motion planning and a collision check are performed to asses if the grasp configuration of the system is suitable. The paper includes application examples of the proposed approach.

The paper deals with the problem of designing an RRT*-based planning algorithm that allows the user to guide the tree growth in a simple and transparent way. The key idea of the proposal is to create a planning algorithm, called HG-RRT*, that minimizes an optimization function over the configuration space where a state cost function is established. This state cost is defined as the combination of several potential fields. Each of these potential fields will attract the solution path or move it away from certain areas. The planning algorithm will try to minimize the path length, the motion effort and the variations of the cost along the path. The paper presents a description of the proposed approach as well as simulation results from a conceptual and an application example, including a thorough comparison with the TRRT planning algorithm.

This work deals with the problem of motion planning for a robotic system with two arms, considering the possibility of using one arm to remove potential obstacles in order to get a collision-free path to reach a desired object with the other arm. The paper compares different motion planning algorithms based on random sampling methods. In the used framework the planners do not discard the samples that imply collision with removable objects, instead these samples are classified according to the obstacles that produce a collision and then processed to decided whether it is necessary to remove these obstacles to get a proper free path. The efficiency of the motion planners are compared by solving planning problems in three different scenarios.

This paper deals with the problem of determining independent contacts regions (ICRs) on 2D articulated objects, such that a finger contact in each region guarantees a force-closure (FC) immobilization, independently of the exact position of the finger. These regions allow a robust finger or fixture placement on the links of the articulated object, despite of possible errors in the position of the contacts. The proposal defines a generalized wrench space for articulated objects and then computes the ICRs starting from an initial FC grasp, considering frictional contacts. The approach has been implemented, and some illustrative examples are provided.

The paper deals with the problem of planning movements of dual-arm anthropomorphic systems, with the aim of reducing the computational cost of the problem and making the movements look as human-like as possible. The key idea of the proposal is the search of synergies of the dual-arm anthropomorphic system in order to use them to reduce the dimension of the search space while preserving human-like appearance. This idea was already developed and successfully used to plan movements of robotic hands, thus the extension to a dual-arm system is attractive. The paper presents a description of the proposed approach as well as real experimental results that encourage doing further research in this line.

This paper proposes a novel motion planning approach that exploits the concept of synergies (correlations) between degrees of freedom, extending it to the velocity space and calling them first-order synergies. An automatic partition method is defined to optimally divide the configuration space into cells where first-order synergies are significantly different. Using this partition, an algorithm that tends to grow a tree by extending the branches in the directions determined by the the first-order synergies of the cell where the leaf to be grown lies is introduced and called FOS-RRT. This allows the natural expansion of the tree along the directions determined by the data used to define the synergies. 2D examples illustrate the performance of the proposed approach, which is particularly attractive for potential applications in human-like robots using human synergies.

This work proposes an approach to manipulate unknown objects based on tactile information. The manipulation can have three goals: the optimization of the hand configuration, the optimization of the grasp quality and the optimization of the object configuration. Three different motion strategies are introduced in order to move the fingers trying to deal with each of the three goals. The strategies can be applied independently or combined in a sequential way. The feasibility of the motion strategies was proven in real experimentation using the Schunk Dexterous Hand SDH2.

There is an increasing need of synchronization and clock frequency distribution in core and access networks. Synchronous Ethernet (SyncE) is a technology that is able to distribute clock frequency over Ethernet networks, while keeping scalability and reduced costs. Software-Defined Networking (SDN) is another emerging technology that is changing the landscape of network management by separating the control and data planes and introducing a centralized management architecture, with the goal of optimizing network operations. OpenFlow is currently the most popular southbound interface between the control and data planes in SDN networks. In order to operate SyncE networks under the new SDN paradigm and exploit its advantages, an extension of OpenFlow messages and operations is needed. This paper discusses how SDN and SyncE can interoperate, proposes a set of OpenFlow extensions that are backwards compatible with SyncE equipment, and presents results from a an implementation.

The paper presents an approach to manipulate unknown objects based on tactile information and force feedback. The object manipulation is performed using two fingers of the Shunck Dexterous Hand, which is equipped with tactile sensors on the fingertips. The contact point on each fingertip is modeled using a virtual articulation which adds a virtual degree of freedom to the finger. The approach uses the tactile data and hand kinematics information in order to estimate a grasp quality measure and to make finger adjustments after an initial grasp in order to improve the hand comfort. The approach was implemented in a real sensorized hand, and some examples manipulating different objects are presented in the paper showing the evolution of the resulting quality.

This work presents an optimization method applied to robot coordination using temporal synchronization. The coordination process considers the possibility of using multi-robot systems in which each robot executes individually planned tasks in a shared environment. The coordination process generates a curve in a discretized coordination space that contains the sequence of coordinated configurations of the robots, this curve can be optimized in order to minimize the backward movements of the robots during their path execution. The optimization method was implemented for a two arm robotic system, a comparison between the executions with and without optimization was performed, and two illustrative experiments are presented in this paper.

The planning of collision-free paths of a team of mobile robots involves many degrees of freedom and therefore the use of sampling-based methods is a good alternative. Among them, the RRT∗ planner has been proposed to cope with optimization problems, and has been proven to be asymptotically optimal. Any optimization function can be defined, although optimization has been usually focused on the traveled distance or on safety, i.e. to find paths of minimum length or maximum clearance. Other constraints to be considered are related to the coordinate movements of the robots, including aspects like keeping a desired formation o having some similar behavior. In this paper we propose the use of an RRT∗ to optimize the traveled distance but subject to a coupled behavior between robots, i.e. it is desired that the robots behave as a group with similar or coordinated movements. To achieve so, a cost function has been defined that evaluates the alignment of the edges of the RRT∗ with the vectors that define the coupling between the motion directions of the robots. The method establishes a compromise between the independence required to avoid obstacles in a flexible way and the desired coupling to behave as a team. The method is illustrated with several examples.

This paper proposes a solution to the problem of grasp analysis and synthesis of 2D articulated objects with 2 and 3 links considering frictionless contacts. The boundary of each link of the object is represented with a finite set of points. The grasp analysis is carried out to verify whether a set of contact points on the object boundary allows a force-closure grasp. The grasp synthesis implies the determination of a set of contact points that allows a force-closure grasp. The paper describes how to find the elements of the generalized wrench vector generated by a force applied on any link of the articulated object. The approach have been implemented and some illustrative examples are included in the paper.

This paper proposes a solution to the problem of coordinating multi-robot systems, which execute individually planned tasks in a shared workspace. The presented approach is a decoupled method that can coordinate the participants robots in on-line mode. The coordination is achieved through the adjustment of the time evolution of each robot along its original planned path according to the movements of the other robots to assure a collision free execution of their tasks. To assess the proposed approach a two robot system was used, and different tests were performed in graphical simulations as well as in real executions. Some examples are presented in the paper.

The paper deals with the problem of planning movements of a two-hand system, considering the possibility of using one hand to remove potential obstacles in order to grasp a desired object with the other hand. The approach is based on a Probabilistic Road Map that does not rule out samples implying collisions with removable objects but instead classify them according to the collided obstacle(s), and allows the search of free paths with the indication of which objects must be removed from the workspace to make the path be actually valid. The approach has been implemented and different tests were performed with considering a real two-hand robotic system with one hand in charge of grasping a desired object and the other in charge of removing the potential obstacles. Some running examples both in simulation and a real workcell are presented in the paper using simulations and real experimentations.

Teleoperating a robotic hand with the aid of a sensorized glove presents some particular problems. A certain problem is due to the kinematic differences between the human hand and the robotic hand, which do not allow a simple direct mapping of the sensor readings from the glove to the robotic hand. This problem is addressed with different types of mapping, but none of them is of general use. This paper proposes two new mappings within two existing mapping types, as well as a new hybrid mapping that combines the best features of these existing mapping types. This hybrid mapping allows intuitive free space movements (where the gesture is more important than the precise positions of the fingers) and grasp movements (where the precise positions of the fingers is more important than the gesture), despite kinematic differences between the human hand and the robotic hand. The approach has been implemented, and some illustrative examples are presented in this paper.

This work proposes a solution to the grasp synthesis problem, which consist of finding the best hand configuration to grasp a given object for a specific manipulation task while satisfying all the necessary constraints. This problem had been divided into sequential sub-problems, including contact region determination, hand inverse kinematics and force distribution, with the particular constraints of each step tackled independently. This may lead to unnecessary effort, such as when one of the problems has no solution given the output of the previous step as input. To overcome this issue, we present a kinestatic formulation of the grasp synthesis problem that introduces compliance both at the joints and the contacts. This provides a proper framework to synthesize a feasible and prehensile grasp by considering simultaneously the necessary grasping constraints, including contact reachability, object restraint, and force controllability. As a consequence, a solution of the proposed model results in a set of hand configurations that allows to execute the grasp using only a position controller. The approach is illustrated with experiments on a simple planar hand using two fingers and an anthropomorphic robotic hand using three fingers.

Independent Contact Regions provide robustness in front of finger positioning errors during an object grasping. However, different sources of uncertainty may be present like, for instance, the friction model used in grasp planning, indetermination of the friction coefficients, and errors in the model of the object that affect the positions of the boundary points as well as the direction normal to the object surface. These sources have not been previously considered in the computation of the Independent Contact Regions. This paper discusses how to take into account these factors when computing the Independent Contact Regions for discretized objects, i.e. objects described with a cloud or a mesh of points. The considerations provided allow a more robust result for application in grasp synthesis and regrasp planning.

Software development in robotics is a complex task due to the existing heterogeneity in terms of hardware, communications, and programming languages that are used in current robotic systems. In this work a general environment for the interaction between the human operator and different elements in a robotized cell is presented, such that all the involved elements can be easily managed from a unique interface. The aim of the developments is to provide a common frame that can be ported to different operating systems and can be easily extended or adapted to new devices.

The paper deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions. The proposed approach tries to mimic the real human hand motions, but reducing the dimension of the search space in order to obtain results as a compromise between motion optimality and planning complexity (time) by means of the concept of principal motion directions. Basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a sampling-based roadmap planner. The approach has been implemented for a four finger anthropomorphic mechanical hand, and some examples are included to illustrate its validity.

This paper presents an approach for quasi-static regrasp planning using n fingers, taking advantage of a method that quickly explores the grasp space for discrete objects. The approach relies on a sampling method, which provides samples of force-closure or non force-closure grasps used to compute regions of the graspable or non-graspable space, respectively. The regrasp contact points generated assure that a force-closure grasp is always possible when performing the regrasp motions. Application examples are included to show the relevance of the results.

The planning of collision-free motions of a hand-arm system to reach a grasp or preshape configuration is not a simple issue due to the high number of involved degrees of freedom. This paper presents an efficient sampling-based path planner that copes with this issue by considering a reduced search space. The dimension of this space is not fixed but it is iteratively increased according to the difficulty of the task at hand. Initially the search space is 1-dimensional along the line defined by the initial and goal hand configurations (by construction those configurations always belong to the search space), and then its dimension is increased by iteratively adding principal motion directions (that couple the finger motions), trying in this way to produce hand movements through anthropomorphic natural postures.

This paper proposes a new and more efficient solution to the problem of determining optimal form-closure constraints of polygonal objects using four contacts. New grasp parameters are determined based only on the directions of the applied forces, which are then used to determine the optimal grasp. Given a set of contact edges, using an analytical procedure a solution that is either the optimal one or is very close to it is obtained (only in this second case an iterative procedure is needed to find a root of a nonlinear equation). This procedure is used for an efficient determination of the optimal grasp on the whole object. The algorithms have been implemented and numerical examples are shown.

Precision grasp synthesis has received a lot of attention in past few last years. However, real mechanical hands can hardly assure that the fingers will precisely touch the object at the computed contact points. The concept of independent contact regions (ICRs) was introduced to provide robustness to finger positioning errors during an object grasping: A finger contact anywhere inside each of these regions assures a force-closure grasp, despite the exact contact position. This paper presents an efficient algorithm to compute ICRs with any number of frictionless or frictional contacts on the surface of any 3-D object. The proposed approach generates the independent regions by growing them around the contact points of a given starting grasp. A two-phase approach is provided to find a locally optimal force-closure grasp that serves as the starting grasp, considering as grasp quality measure the largest perturbation wrench that the grasp can resist, independently of the perturbation direction. The proposed method can also be applied to compute ICRs when several contacts are fixed beforehand. The approach has been implemented, and application examples are included to illustrate its performance.

This paper presents an efficient algorithm to compute independent contact regions on the surface of complex 3D objects such that a finger contact anywhere inside each of these regions assures a force-closure grasp despite the exact contact position. Independent contact regions provide robustness in front of finger positioning errors during an object grasping, and give relevant information for finger repositioning during the object manipulation. The object is described with a mesh of surface points, so the procedure is applicable to objects of any arbitrary shape. The proposed approach uses information from the wrench space, and generates the independent regions by growing them around the contact points of a given starting grasp. A two-phase approach is also provided to find a locally optimum force-closure grasp that serves as starting grasp, considering as grasp quality measure the largest perturbation wrench that the grasp can resist with independence of the perturbation direction. The approach has been implemented and several examples are provided to illustrate its performance.

Planning a precision grasp for a robot hand is usually decomposed into two main steps. First, a set of contact points over the object surface must be determined, ensuring they allow a stable grasp. Second, the inverse kinematics of the robot hand must be solved to verify whether the contact points can actually be reached. Whereas the first problem has been largely solved in a general posing, the second one has only been tackled with local convergence methods. These methods only provide one solution to the problem, even if many are possible, and depending on the initial estimation they use, they may fail to converge, which results in grasp re-planning in situations where it could be avoided. This paper overcomes both issues by providing a complete method to solve the kinematics of human-like hands. The method is able to find all possible configurations that reach the specified contact points, even when positive-dimensional sets of such configurations are possible.

The paper presents a method to build force-closure grasps on polyhedral objects with four frictional contact points such that three of the fingers can individually lose the contact with the object without making the three finger remaining grasp losing the force-closure property. This type of grasps is useful to allow the object manipulation via regrasp using finger gating. A necessary and sufficient condition for the determination of these grasps is proposed, as well as the algorithm to compute them.

This paper presents the use of independent contact and non-graspable regions to generate the grasp space for 2D and 3D discrete objects. The grasp space is constructed via a sampling method, which provides samples of force-closure or non force-closure grasps, used to compute regions of the graspable or non-graspable space, respectively. The method provides a reliable procedure for an efficient generation of the whole grasp space for n-finger grasps on discrete objects; two examples on 2D objects are provided to illustrate its performance. The approach has several applications in manipulation and regrasping of objects, as it provides a large number of force-closure and non force-closure grasps in a short time.

Grasp synthesis on real 3D objects is a critical problem in grasp and manipulation planning. This paper presents a geometrical approach to compute force closure (FC) grasps, with or without friction and with any number of fingers. The object's surface is discretized in a cloud of points, so the algorithm is applicable to objects of any arbitrary shape. One or more FC grasps are obtained with a geometrical approach, which embeds the FC test in the algorithm to simplify achieving the force-closure property. This initial FC grasp may be improved with a complementary optimization algorithm. The grasp quality is measured considering the largest perturbation wrench that the grasp can resist with independence of the perturbation direction. The efficiency of both algorithms is illustrated through numerical examples.

This paper presents a new approach to obtain three-finger robust grasps of 2D irregular objects. Given a discrete description of the object boundary, a partial representation of the force-closure space considering the position of two contact points is obtained. The computational cost of this representation is low and it still includes enough information to obtain suitable grasps as well as independent regions on the object boundary, such that a finger in each region ensures a force-closure grasp independently of the exact position of the contact points. The procedure has been implemented and several examples of the proposed methodology are included in the paper

Force-closure independent regions are parts of the object edges such that a grasp with a finger in each region ensures a force-closure grasp. These regions are useful to provide some robustness to the grasp in the presence of uncertainty as well as in grasp planning. Most of the approaches to the computation of these regions for N fingers work on the contact space, implying a N-dimensional problem. This paper presents a new approach to determine independent regions on polygonal objects considering N friction or frictionless contacts. The approach works on the object space, implying that it is always a two-dimensional problem and, since it is not necessary to compute all the force-closure space, it becomes a very fast approach. Besides, the approach is also flexible since constraints on the fingers placement can be easily introduced. Some graphical examples are included in the paper showing the simplicity of the methodology.

Given an industrial robot equipped with a dexterous hand and an object to be grasped with four grasping points determined on its faces, this paper deals with the problem of finding the joint configurations that allow to grasp that object. The proposed solution is based on an iterative optimization method that consecutively moves the joint that best contributes to reduce the distance of the fingertips to the desired locations. The method is particularized for a Stäubli RX90 robot and the dexterous hand MA-I with four fingers developed at the IOC’s Robotics Lab.

This paper deals with the problem of determining independent grasps regions on the object boundary such that a four frictionless grasp with a contact point in each region assures a form-closure grasp independently of the exact position of the contact point. These regions are useful to provide some robustness to the grasp in front of finger positioning errors as well as in the design of fixtures. Given a discrete description of a 2D object, the methodology takes into account the uncertainty in the object description and it determines the independent regions without using hard iterative search procedures. The procedure has been implemented and an example of the proposed methodology is included in the paper.

The paper deals with the determination of optimal force-closure grasps for 2D polygonal objects. The problem is analyzed and some intrinsic properties of grasps are determined. The approach is applied to the determination of the position of a fourth finger given the positions of three other fingers. Moreover, the range of solutions that allow the force-closure property as well as the optimal value are analytically determined, checking only four points in the worst case. The algorithm has been implemented and numerical examples are included in the paper.

The performance of tasks with robots in environments with low clearances (e.g. robotized assembly) is usually difficult, due mainly to geometric uncertainty and tolerances. Following a pragmatical approach to planar constrained-motion planning, this paper proposes a method for checking the feasibility of paths generated by gross-motion planning algorithms, taking into account the uncertainties affecting the task and the use of a compliant control mode. The method enables the extension of gross-motion planning techniques to constrained-motion planning problems, ensuring the feasibility of the task despite the uncertainties.

This paper deals with the assembly line balancing problem considering incompatibilities between the tasks with the aim of: first minimizing the number of workstations, and then minimizing the cycle time for the minimum number of workstations. In order to solve the problem we propose the use of a greedy randomized adaptive search procedure obtained from the application of some classic heuristics based on priority rules, and a genetic algorithm that searches for the solution in the heuristic space. A computational experience is included to illustrate the performance of the proposed approach.

The use of multiple robots sharing a workspace can increase the productivity and the versatility of complex applications, making the existence of cells with several robots more common. On the other hand, when the robots are used to perform independent tasks in a shared workspace each one becomes a mobile obstacle for the other. Several methods have been proposed to deal with the problem of robot coordination in order to avoid collisions in these situations. The paper analyzes these methods identifying the basic tools used in each one and trying to unify the nomenclature. Illustrative works are listed and classified. The classification of the different approaches can be useful for future developments in the field.

This paper presents a method for the planning and execution of compliant motions within the scope of a two-phase fine-motion planner for the performance of planar assembly tasks with robots. Algorithms are provided to find a nominal solution path in both free and contact configuration space which is feasible in spite of the uncertainties affecting the task. Compliant-motion commands based on the generalized damping control mode are synthesized to follow this path, allowing to maintain a constant bounded force.

This paper presents the graphical embedding of the rotational degree of freedom into the translational configuration space for planar assembly tasks. The resulting representation helps the visualization and understanding of motions in a simple and easy graphical way. The aim is the developing of motion planning algorithms in a two-dimensional parametrized space for planar assembly tasks (3 DOF) and in a three-dimensional parametrized space for assembly tasks in space (6 DOF). The method has been successfully applied to the problem of contact identification in the presence of uncertainty for planar assembly tasks. The extension to six degrees of freedom is under development.

A statistical measure of the cost of assembling two mating features is presented. It is based on how well the geometry of the task compensates, during the assembly, the uncertainties associated with the mating features: manufacturing tolerances and the pose uncertainty. The latter is due either to the accumulation of tolerances and clearances between mating features that are previously assembled or to the positioning errors of an assembly robot. The proposed measure allows a systematic evaluation of the assembly cost for a given assembly sequence, thus providing a method for evaluating assembly sequences and selecting an optimal one from the assembly point of view. The influence of different sources of uncertainties and the assembly procedure can also be evaluated with the proposed cost index. The paper includes an example to illustrate the use of the proposed cost measure.

Force-compliant control is an important aspect for performing assembly tasks with robots, since the geometric constraints of the task are usually used as guides. The paper presents the specification of compliant motions for planar assembly tasks (two degrees of freedom of translation and one of rotation), taking into account the effect of friction.

An assembly task, whether robotic or manual, may involve adjusting the positions of already assembled parts when a new part is assembled, due to tolerance and position uncertainties of the parts. Such an adjusting operation can increase the cost of the product because of the adjusting time, fixturing costs, additional operations, etc. In this paper, a statistical approach to evaluate the cost of assembly is proposed. The cost is measured according to the number of objects that must be moved in order to successfully assemble the product. The approach uses a statistical analysis of the accumulated tolerances, the local clearances, and the possible adjustable zones due to clearances between the already assembled parts, to calculate the minimum number of objects that must be adjusted to complete the assembly. Special attention is given to the assembly of parallel chains. The complete procedure and simulation results are given.