Tethered Tool Manipulation Planning With Cable Maneuvering

Sánchez, Daniel; Wan, Weiwei; Harada, Kensuke

doi:10.1109/lra.2020.2974675

Cited by 21 publications

(13 citation statements)

References 28 publications

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“…Previously, many projects were developed using grasping poses planned by the algorithms in this article. These projects use the planned grasps to iteratively select start and goal poses in the robot workspace [43]- [45], build manipulation graphs [46]- [50], or optimize object assembly sequences [51]- [53]. Although the proposed algorithms were heavily used in these projects, they were not carefully introduced or analyzed.…”

Section: Relations To Our Previous Studiesmentioning

confidence: 99%

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

2021

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This article develops model-based grasp planning algorithms. It focuses on industrial end-effectors like grippers and suction cups, and plans grasp configurations considering computer aided design (CAD) models of target objects. The developed algorithms can stably find many high-quality grasps, with satisfying precision and little dependency on the quality of CAD models. The undergoing core technique is superimposed segmentation, which preprocesses a mesh model by peeling it into superimposed facets. The algorithms use the facets to locate contacts and synthesize grasp poses for popular industrial end-effectors. Several tunable parameters are prepared to adapt the algorithms to meet various requirements. The experimental section studies the influence of the tunable parameters and analyzes the cost, precision, and robustness of the proposed algorithms and their planned grasps, with both simulations and real-world systems. Besides, the proposed algorithms are applicable to mesh models reconstructed from point clouds obtained by depth sensors. Some experiments and analysis are also carried out to study and demonstrate the ability. Index Terms-Grasping, grippers and other end-effectors, manipulation planning. I. INTRODUCTIONT HIS article develops algorithms to plan grasping configurations automatically. It focuses on industrial end-effectors, and plans grasp poses for these end-effectors considering computer aided design (CAD) models of target objects.Developing grasp planning algorithms is vital to manufacturing using "teachingless" robotic manipulators. Modern robotic manipulation systems use manually annotated or pretaught grasp configurations to perform specific tasks, which is not only costly Manuscript

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Section: Relations To Our Previous Studiesmentioning

confidence: 99%

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

2021

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“…However, the angle constraints do not directly prevent cable entanglements. To address cable entanglements, we use a method to predict the magnitude of cable "snarling" or cable movement around the robot arm during simulation [31]. By measuring the angle accumulation of every robot state during the planning stage and preventing the accumulation from surpassing a given threshold, it is possible to create entanglement-free motion sequences for tool manipulation.…”

Section: Angle Accumulation Constraintsmentioning

confidence: 99%

“…The concept of angle accumulation [31] represents the degree of rotation or movement of the tool cable around the robot arm. For simplicity, our planner uses polar coordinates in the tool reference frame to quantify the movement of the cable.…”

Section: Angle Accumulation Constraintsmentioning

confidence: 99%

Towards Tethered Tool Manipulation Planning with the Help of a Tool Balancer

2020

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Handling and maneuvering tools across a robot workspace is a challenging task that often requires the implementation of constrained motion planning. In the case of wired or tethered tools, their maneuvering becomes considerably harder by the tool cable. If the cable presence is not considered, the robot motions may make the cable become entangled with the robot arms or elements of its workspace, causing accidents or unnecessary strain on the robot and the tool. Furthermore, the behavior of the tool cable during manipulation and its degree of entanglement around the robot are difficult to predict. The present paper introduces a constrained manipulation planner for dual-armed tethered tool manipulation involving tool re-grasping. Our solution employs a tool balancer to straighten the tool cable and facilitate the cable deformation problem. The planner predicts the cable states during manipulation and restricts the robot motions in order to avoid cable entanglements and collisions while performing tool re-posing tasks. Furthermore, the planner also applies orientational constraints to limit the cable bending, reducing the torque and stress suffered by the robot due to the cable tension. Simulations and real-world experiments validated the presented method.

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“…Robot manipulation of 1D deformable objects, such as cables, ropes, and wires, can facilitate automation of tasks in industrial, surgical, and household settings [9]. Such manipulation tasks include organization of cables [7,27], hoses [23], suture thread [31], and wires used in automotive and other electronic assembly [12], as well as reducing clutter and preventing injury in surgical, manufacturing, and home environments [2]. As robots automate more tasks involving 1D deformable objects, which we refer to as "cables," they will increasingly confront highly complex knots, either because the task itself requires knot untangling or because untangling cables is a prerequisite for a downstream task.…”

Section: Introductionmentioning

confidence: 99%

Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies

Sundaresan

Grannen

Thananjeyan

et al. 2021

Robotics: Science and Systems XVII

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Robot manipulation for untangling 1D deformable structures such as ropes, cables, and wires is challenging due to their infinite dimensional configuration space, complex dynamics, and tendency to self-occlude. Analytical controllers often fail in the presence of dense configurations, due to the difficulty of grasping between adjacent cable segments. We present two algorithms that enhance robust cable untangling, LOKI and SPi-DERMan, which operate alongside HULK, a high-level planner from prior work. LOKI uses a learned model of manipulation features to refine a coarse grasp keypoint prediction to a precise, optimized location and orientation, while SPiDERMan uses a learned model to sense task progress and apply recovery actions. We evaluate these algorithms in physical cable untangling experiments with 336 knots and over 1500 actions on real cables using the da Vinci surgical robot. We find that the combination of HULK, LOKI, and SPiDERMan is able to untangle dense overhand, figure-eight, double-overhand, square, bowline, granny, stevedore, and triple-overhand knots. The composition of these methods successfully untangles a cable from a dense initial configuration in 68.3% of 60 physical experiments and achieves 50% higher success rates than baselines from prior work. Supplementary material, code, and videos can be found at https://tinyurl.com/rssuntangling.

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Tethered Tool Manipulation Planning With Cable Maneuvering

Cited by 21 publications

References 28 publications

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

Towards Tethered Tool Manipulation Planning with the Help of a Tool Balancer

Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies

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