The paper proposes a Δ-Y transformations technique for stiffness modelling of over-constrained manipulators with internal cross-linkages. It allows representing complex structures as a serial-parallel equivalent one that can be easily handled by the VJM-based method. To derive desired analytical expressions for the equivalent serial-parallel structure, the MSA-based stiffness modelling approach is employed first, which allows describing the stiffness response for both the Δ and Y structures operating with VJM-type stiffness matrices. Further, the desired relations between equivalent Δ-Y and Y-Δ stiffness matrices are obtained. The example of stiffness modelling of a non-rigid Gough-Stewart platform with multiple cross-linkages demonstrates the benefits of the proposed technique.

This paper introduces a multi-stage approach to enhance homogeneous thickness distribution in single-point incremental sheet forming. Traditional methods often struggle with achieving uniform material thickness, compromising the final part’s structural integrity and performance. Our proposed methodology leverages a V-shape starting stage, systematically designed to address these challenges by improving material flow and distribution throughout the forming process. We employ finite element analysis to validate the efficiency of the proposed approach and compare it with conventional single-stage forming processes. The simulations demonstrate significant improvements in achieving a more uniform thickness distribution across the formed parts. The comparative analysis highlights the potential of the proposed multi-stage approach for industrial applications and enhances material homogeneity. This methodology advances the precision and reliability of formed parts and opens new avenues for research and development in sheet metal forming technologies.

The paper deals with stiffness matrix identification of complex-shape robotic links. It proposes an enhancement that simplifies the identification procedure by splitting the link into simple segments for which the identification procedure is trivial. Further, the segments’ stiffness matrices are aggregated into the desired link stiffness matrix. The proposed enhancement allows users to avoid ambiguity with reference points for link connections with multiple surfaces. The developed technique is applied to real-world problem dealing with tool stiffness matrix identifications from the CAD-based virtual experiments.

The springback effect refers to the tendency of a material to partially return to its original shape after deformation, especially in incremental metal forming processes. It poses a significant challenge in achieving precise and accurate final shapes, often requiring compensation strategies to reduce its impact. This study focuses on the evaluation and development related to a springback compensation model for incremental sheet metal forming processes. The proposed model aims to optimize toolpaths to counteract springback by addressing deviations between the formed and intended shapes. Performance assessment involved offline numerical simulations using Abaqus/CAE software across various materials. Results demonstrate significant efficacy, with mean error reductions exceeding 60% across different materials. Additionally, the model maintains sheet thickness, preserving material strength during forming.

The work focuses on the development of an adaptive technique for the physical interaction handling between a human and a robot, as well as its experimental validation. The proposed technique is based on the deep residual neural network and dedicated finite state machine, where the states are the robot behavior modes and transitions are the switchings between the states that depend on the interaction parameters and characteristics. It ensures the human operator safety and improves the human–robot collaboration performance by implementing various scenarios. In the scope of this technique, the parameters of human–robot interaction are used to select an appropriate robot reaction strategy using data from internal robot sensors only, i.e. proprioceptive sensors. These parameters define the interaction force vector and its application point on the robot surface, which allow to classify the interaction within the set of predefined categories. This classification distinguishes interactions applied at the tool or intermediate link (Tool/Link), having soft or hard nature (Soft/Hard), as well as having different intention (Intl/Accd) or duration (Short/Long). Based on identified category and the current robot state, the algorithm chooses an appropriate robot reaction. To confirm the efficiency the developed technique, an experimental study was conducted, which involved the collaboration between the real industrial manipulator KUKA LBR iiwa and the human operator.

In this work, we present a new exploration algorithm for Micro Aerial Vehicles (MAVs). The planner uses a combination of Next-Best-View (NBV) sampling and frontier-based approaches to reduce the impact of finding unexplored areas in large scenarios. For each sampled point, the yaw angle is optimized to maximize the potential gain for mapping. The gain is expressed as a ratio between the exploration objective and the time it would take to reach the pose, thus, balancing the nearby exploration and global coverage. We reduce the gain computation bottleneck in the sampling strategy by managing a dual-map with different resolutions. The planner maintains a history graph, with nodes that indicate regions of interest for map expansion. We demonstrate the abilities of the proposed algorithm with simulations and real-world experiments. The results outperform state-of-the-art methods in exploration time and computational cost.

The goal of this work is to propose a way of dealing with physical interactions for collaborative robots that will ensure the safety of a human operator and improve the performance of a common task by implementing multiple robot behavior scenarios. In this scope, all collisions of a robotic arm are detected and analyzed to chooses an appropriate reaction strategy. The points of contact on the robot’s surface for each collision are estimated, the external forces are identified and collisions are classified by the set of predefined categories. Based on these categories and the current robot state, the algorithm chose an appropriate behavior scenario.All presented algorithms are based only on proprioceptive sensors information and were tested in a simulated environment and on the real collaborative robots KUKA iiwa and Universal Robots UR10e. The result for contact localization showed 4 cm mean accuracy, the classification algorithm was able to identify collisions with hard and soft objects with 98% accuracy for KUKA iiwa 14.

International audience

The paper presents an approach for interactive programming of the robotic manipulator using mixed reality. The developed system is based on the HoloLens glasses connected through Robotic Operation System to Unity engine and robotic manipulators. The system gives a possibility to recognize the real robot location by the point cloud analysis, to use virtual markers and menus for the task creation, to generate a trajectory for execution in the simulator or on the real manipulator. It also provides the possibility of scaling virtual and real worlds for more accurate planning. The proposed framework has been tested on pick-and-place and contact operations execution by UR10e and KUKA iiwa robots.

High dynamic capabilities of industrial robots make them dangerous for humans and environment. To reduce this factor and advance collaboration between human and manipulator fast and reliable collision detection algorithm is required. To overcome this problem, we present an approach allowing to detect collision, localize action point and classify collision nature. Internal joint torque and encoder measurements were used to determine potential collisions with the robot links. This work proposes two ways of solving this problem: using classical analytical approach and learning approach implemented with neural network. The suggested algorithms were examined on the industrial robotic arm Kuka iiwa LBR 14 R820, ground truth information on the contact nature and its location were obtained with 3D LIDAR and camera.

The problem of walking trajectory optimisation for bipedal humanoid robots attracts many researchers because of excessive interest to bipedal locomotion. The main focus is usually on robot dynamics and trajectory planning for predefined walking primitives. In contrast to other works, our paper targets to obtain optimal walking primitive for swing leg trajectory of bipedal humanoid robot walking. Optimal walking primitives are obtained taking into account velocity and acceleration physical limitations of each joint and are derived for different walking parameters such as step size and hip height. To obtain a desired time-optimal trajectory dynamic programing approach is used. It is shown that a new trajectory is performed within a shorter time comparing with commonly used locomotion trajectories for bipedal robots control. The results allow us to assign walking parameters and corresponding walking primitive that maximize robot velocity for predefined environment constraints.

The paper deals with comparison study of serial and quasi-serial industrial robots used for machining operations. It proposes a new methodology for robot ranking, which is based on estimation of the end-effector resistance to cutting forces for several machining tasks, which are optimally located within the robot workspace. To cover wide range of applications, a set of isotropic, quasi-isotropic and extended benchmark tasks are considered. It is shown that regardless of the benchmark problem, seral manipulators are preferable for small and medium tasks while quasi-serial ones' better suit large-dimensional tasks. The proposed technique was applied for the comparison analysis of 10 industrial robots of both serial and quasi-serial architectures with similar working radius and payload of about 200 kg.

The paper addresses a problem of robotic manipulator calibration. The main contributions are in the area of the elastostatic parameters identification. In contrast to other works, the considered approach takes into account elastic properties of both links and joint. Particular attention is paid to generation of the complete and irreducible stiffness model that is suitable for the identification. To solve the problem, physical and algebraic model reduction methods are proposed. They are based on taking into account the physical properties of the manipulator elements and structure of the corresponding observation matrix. The advantages of the developed approach are illustrated by an application example that deals with elastostatic calibration of an industrial robot.

The paper focuses on the stiffness modeling of heavy industrial robots with gravity compensators. The main attention is paid to the identification of geometrical and elastostatic parameters and calibration accuracy. To reduce impact of the measurement errors, the set of manipulator configurations for calibration experiments is optimized with respect to the proposed performance measure related to the end-effector position accuracy. Experimental results are presented that illustrate the advantages of the developed technique.

The paper focuses on the stiffness modeling of parallel manipulators composed of non-perfect serial chains, whose geometrical parameters differ from the nominal ones. In these manipulators, there usually exist essential internal forces/torques that considerably affect the stiffness properties and also change the end-effector location. These internal loadings are caused by elastic deformations of the manipulator elements during assembling, while the geometrical errors in the chains are compensated for by applying appropriate forces. For this type of manipulators, a non-linear stiffness modeling technique is proposed that allows us to take into account inaccuracy in the chains and to aggregate their stiffness models for the case of both small and large deflections. Advantages of the developed technique and its ability to compute and compensate for the compliance errors caused by different factors are illustrated by an example that deals with parallel manipulators of the Orthoglide family.

This paper focuses on stiffness matrix computation for manipulators with passive joints, compliant actuators, and flexible links. It proposes both explicit analytical expressions and an efficient recursive procedure that are applicable in the general case and allow us to obtain the desired matrix either in analytical or numerical form. Advantages of the developed technique and its ability to produce both singular and nonsingular stiffness matrices are illustrated by application examples that deal with stiffness modeling of two Stewart-Gough platforms.

The paper focuses on stiffness matrix computation for manipulators with passive joints. It proposes both explicit analytical expressions and an efficient recursive procedure that are applicable in general case and allow obtaining the desired matrix either in analytical or numerical form. Advantages of the developed technique and its ability to produce both singular and non-singular stiffness matrices are illustrated by application examples that deal with stiffness modeling of two Stewart-Gough platforms.