This work demonstrates the utility of dual-arm robots with dual-wrist force-torque sensors in manipulating a Deformable Linear Object (DLO) within an unknown environment that imposes constraints on the DLO’s movement through contacts and fixtures. We propose a strategy to estimate the pose of unknown environmental contacts encountered during the manipulation of a DLO, classifying the induced constraints as unilateral, bilateral and fully constrained, exploiting the redundancy of force sensors. A semantic approach to define environmental constraints is introduced and incorporated into a graph-based model of the DLO. This model remains accurate as long as the DLO is under tension and is dynamically updated throughout the manipulation process, built by sequencing a set of primitives. The estimation strategy is validated through simulations and real-world experiments, demonstrating its potential in handling DLOs under various, possibly uncertain, constraints.

This paper tackles the complex task of routing elastic deformable linear objects (DLOs) characterized by considerable stiffness, such as cables or hoses, which are already constrained at both ends. Specifically, a single arm robot is controlled to slide along the unknown contour of the cable, performing collision-free contour following, and to insert specific DLO segments into intermediate known clips. The contour following motion is executed avoiding both collisions with static obstacles and excessive deformation of the manipulated DLO. In particular, the path is defined considering an artificial potential field that is updated after each sliding motion along the DLO. This field accounts for static obstacles, the local cable shape (reconstructed using tactile sensors on the gripper fingertips) and the estimation of the global DLO shape obtained from a dynamic model of the DLO, accounting for the constraints imposed by the clips and the gripper. The proposed method is experimentally validated on an industrial robot executing cable routing in several DLO configurations.

This paper proposes a set of tactile based skills to perform robotic cable routing operations for deformable linear objects (DLOs) characterized by considerable stiffness and constrained at both ends. In particular, tactile data are exploited to reconstruct the shape of the grasped portion of the DLO and to estimate the future local one. This information is exploited to obtain a grasping configuration aligned to the local shape of the DLO, starting from a rough initial grasping pose, and to follow the DLO's contour in the three-dimensional space. Taking into account the distance travelled along the arc length of the DLO, the robot can detect the cable segments that must be firmly grasped and inserted in intermediate clips, continuing then to slide along the contour until the next DLO's portion, that has to be clipped, is reached. The proposed skills are experimentally validated with an industrial robot on different DLOs in several configurations and on a cable routing use case.

This paper deals with the challenging task of picking semi-deformable linear objects (SDLOs) from a bin. SDLOs are deformable elements, such as cables, joined to a rigid part as a connector. We propose a vision-based strategy to detect, classify and estimate the pose and the state (free or occluded) of connectors belonging to an unspecified number of SDLOs, arranged in an unknown configuration in the bin. The connectors can then be grasped and manipulated by a dual-arm robot through a set of manipulation primitives. In this way, a single SDLO can be extracted from the bin and laid on the worktable. A subsequent association between the connectors and the extracted SDLOs is performed, allowing to firmly grasp a SDLO at its ends to further manipulate it. The procedure is tested in bin picking operations with several kinds of SDLOs and is applied to a use case involving a collaborative wire harnesses assembly task.

In this work, we address two crucial issues that arise in the design of a human-robot collaborative station for the assembly of products: the optimal task allocation and the scheduling problem. We propose an offline method to solve in series the two mentioned issues, considering a static allocation and taking into account several features such as the minimization of postural discomfort, operation processing times, idle times and hence the total cycle time. Our methodology consists of a mixed approach that combines a capability-based method, where the agents' capabilities are tested against a list of predefined criteria, with optimization. In particular, we formulate a modified version of the Hungarian Algorithm to solve also unbalanced assignment problems, where the number of tasks is different from the number of agents. The scheduling policy is obtained by means of a Mixed Integer Linear Programming (MILP) formulation, with a multi-objective optimization. Moreover, the concepts of operation, assembly tree and precedence graph are formalized, since they represent the inputs to our method, together with the information on the workstation layout and on the selected kind of robot. Finally, the proposed solution is applied to a case study to define the optimal task allocation and scheduling for two different workstation layouts: the results are compared and the best layout is accordingly selected.