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Register a new userRobotic-assisted Ultrasound for Fetal Imaging: Evolution from Single-arm to Dual-arm System
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Added by
Shuangyi Wang
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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The development of robotic-assisted extracorporeal ultrasound systems has a long history and a number of projects have been proposed since the 1990s focusing on different technical aspects. These aim to resolve the deficiencies of on-site manual manipulation of hand-held ultrasound probes. This paper presents the recent ongoing developments of a series of bespoke robotic systems, including both single-arm and dual-a
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In this paper, we present a planner for a master dual-arm robot to manipulate tethered tools with an assistant dual-arm robots help. The assistant robot provides assistance to the master robot by manipulating the tool cable and avoiding collisions. The provided assistance allows the master robot to perform tool placements on the robot workspace table to regrasp the tool, which would typically fail since the tool cable tension may change the tool positions. It also allows the master robot to perform tool handovers, which would normally cause entanglements or collisions with the cable and the environment without the assistance. Simulations and real-world experiments are performed to validate the proposed planner.
This paper presents a planner that can automatically find an optimal assembly sequence for a dual-arm robot to assemble the soma blocks. The planner uses the mesh model of objects and the final state of the assembly to generate all possible assembly sequence and evaluate the optimal assembly sequence by considering the stability, graspability, assemblability, as well as the need for a second arm. Especially, the need for a second arm is considered when supports from worktables and other workpieces are not enough to produce a stable assembly. The planner will refer to an assisting grasp to additionally hold and support the unstable components so that the robot can further assemble new workpieces and finally reach a stable state. The output of the planner is the optimal assembly orders, candidate grasps, assembly directions, and the assisting grasps if any. The output of the planner can be used to guide a dual-arm robot to perform the assembly task. The planner is verified in both simulations and real-world executions.
Standardized evaluation measures have aided in the progress of machine learning approaches in disciplines such as computer vision and machine translation. In this paper, we make the case that robotic learning would also benefit from benchmarking, and present the REPLAB platform for benchmarking vision-based manipulation tasks. REPLAB is a reproducible and self-contained hardware stack (robot arm, camera, and workspace) that costs about 2000 USD, occupies a cuboid of size 70x40x60 cm, and permits full assembly within a few hours. Through this low-cost, compact design, REPLAB aims to drive wide participation by lowering the barrier to entry into robotics and to enable easy scaling to many robots. We envision REPLAB as a framework for reproducible research across manipulation tasks, and as a step in this direction, we define a template for a grasping benchmark consisting of a task definition, evaluation protocol, performance measures, and a dataset of 92k grasp attempts. We implement, evaluate, and analyze several previously proposed grasping approaches to establish baselines for this benchmark. Finally, we also implement and evaluate a deep reinforcement learning approach for 3D reaching tasks on our REPLAB platform. Project page with assembly instructions, code, and videos: https://goo.gl/5F9dP4.
This work introduces an approach for automatic hair combing by a lightweight robot. For people living with limited mobility, dexterity, or chronic fatigue, combing hair is often a difficult task that negatively impacts personal routines. We propose a modular system for enabling general robot manipulators to assist with a hair-combing task. The system consists of three main components. The first component is the segmentation module, which segments the location of hair in space. The second component is the path planning module that proposes automatically-generated paths through hair based on user input. The final component creates a trajectory for the robot to execute. We quantitatively evaluate the effectiveness of the paths planned by the system with 48 users and qualitatively evaluate the system with 30 users watching videos of the robot performing a hair-combing task in the physical world. The system is shown to effectively comb different hairstyles.
Robotic dual-arm twisting is a common but very challenging task in both industrial production and daily services, as it often requires dexterous collaboration, a large scale of end-effector rotating, and good adaptivity for object manipulation. Meanwhile, safety and efficiency are preliminary concerns for robotic dual-arm coordinated manipulation. Thus, the normally adopted fully automated task execution approaches based on environmental perception and motion planning techniques are still inadequate and problematic for the arduous twisting tasks. To this end, this paper presents a novel strategy of the dual-arm coordinated control for twisting manipulation based on the combination of optimized motion planning for one arm and real-time telecontrol with human intelligence for the other. The analysis and simulation results showed it can achieve collision and singularity free for dual arms with enhanced dexterity, safety, and efficiency.
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