2,873 research outputs found
A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts
This paper presents a multi-robot system for manufacturing personalized
medical stent grafts. The proposed system adopts a modular design, which
includes: a (personalized) mandrel module, a bimanual sewing module, and a
vision module. The mandrel module incorporates the personalized geometry of
patients, while the bimanual sewing module adopts a learning-by-demonstration
approach to transfer human hand-sewing skills to the robots. The human
demonstrations were firstly observed by the vision module and then encoded
using a statistical model to generate the reference motion trajectories. During
autonomous robot sewing, the vision module plays the role of coordinating
multi-robot collaboration. Experiment results show that the robots can adapt to
generalized stent designs. The proposed system can also be used for other
manipulation tasks, especially for flexible production of customized products
and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial
Informatics, Key words: modularity, medical device customization, multi-robot
system, robot learning, visual servoing, robot sewin
A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts
This paper presents a multi-robot system for manufacturing personalized
medical stent grafts. The proposed system adopts a modular design, which
includes: a (personalized) mandrel module, a bimanual sewing module, and a
vision module. The mandrel module incorporates the personalized geometry of
patients, while the bimanual sewing module adopts a learning-by-demonstration
approach to transfer human hand-sewing skills to the robots. The human
demonstrations were firstly observed by the vision module and then encoded
using a statistical model to generate the reference motion trajectories. During
autonomous robot sewing, the vision module plays the role of coordinating
multi-robot collaboration. Experiment results show that the robots can adapt to
generalized stent designs. The proposed system can also be used for other
manipulation tasks, especially for flexible production of customized products
and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial
Informatics, Key words: modularity, medical device customization, multi-robot
system, robot learning, visual servoing, robot sewin
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task specifications. These specifications are fed to a
hierarchical neural program, where bottom-level programs are callable
subroutines that interact with the environment. We validate our method in three
robot manipulation tasks. NTP achieves strong generalization across sequential
tasks that exhibit hierarchal and compositional structures. The experimental
results show that NTP learns to generalize well to- wards unseen tasks with
increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201
Context-aware learning for robot-assisted endovascular catheterization
Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces
Automated pick-up of suturing needles for robotic surgical assistance
Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate
cancer that involves complete or nerve sparing removal prostate tissue that
contains cancer. After removal the bladder neck is successively sutured
directly with the urethra. The procedure is called urethrovesical anastomosis
and is one of the most dexterity demanding tasks during RALP. Two suturing
instruments and a pair of needles are used in combination to perform a running
stitch during urethrovesical anastomosis. While robotic instruments provide
enhanced dexterity to perform the anastomosis, it is still highly challenging
and difficult to learn. In this paper, we presents a vision-guided needle
grasping method for automatically grasping the needle that has been inserted
into the patient prior to anastomosis. We aim to automatically grasp the
suturing needle in a position that avoids hand-offs and immediately enables the
start of suturing. The full grasping process can be broken down into: a needle
detection algorithm; an approach phase where the surgical tool moves closer to
the needle based on visual feedback; and a grasping phase through path planning
based on observed surgical practice. Our experimental results show examples of
successful autonomous grasping that has the potential to simplify and decrease
the operational time in RALP by assisting a small component of urethrovesical
anastomosis
A knowledge-based framework for task automation in surgery
Robotic surgery has significantly improved the quality of surgical procedures. In the past, researches have been focused on automating simple surgical actions, however there exists no scalable framework for automation in surgery. In this paper, we present a knowledge-based modular framework for the automation of articulated surgical tasks, for example, with multiple coordinated actions. The framework is consisted of ontology, providing entities for surgical automation and rules for task planning, and \u201cdynamic movement primitives\u201d as adaptive motion planner as to replicate the dexterity of surgeons. To validate our framework, we chose a paradigmatic scenario of a peg-and-ring task, a standard training exercise for novice surgeons which presents many challenges of real surgery, e.g. grasping and transferring. Experiments show the validity of the framework and its adaptability to faulty events. The modular architecture is expected to generalize to different tasks and platforms
Robot Assisted Object Manipulation for Minimally Invasive Surgery
Robotic systems have an increasingly important role in facilitating minimally invasive surgical treatments. In robot-assisted minimally invasive surgery, surgeons remotely control instruments from a console to perform operations inside the patient. However, despite the advanced technological status of surgical robots, fully autonomous systems, with decision-making capabilities, are not yet available.
In 2017, a structure to classify the research efforts toward autonomy achievable with surgical robots was proposed by Yang et al. Six different levels were identified: no autonomy, robot assistance, task autonomy,
conditional autonomy, high autonomy, and full autonomy. All the commercially available platforms in robot-assisted
surgery is still in level 0 (no autonomy). Despite increasing the level of autonomy remains an open challenge, its adoption could potentially introduce multiple benefits, such as decreasing surgeons’ workload and fatigue and pursuing a consistent
quality of procedures. Ultimately, allowing the surgeons to interpret the ample
and intelligent information from the system will enhance the surgical outcome and
positively reflect both on patients and society. Three main aspects are required to
introduce automation into surgery: the surgical robot must move with high precision,
have motion planning capabilities and understand the surgical scene. Besides
these main factors, depending on the type of surgery, there could be other aspects
that might play a fundamental role, to name some compliance, stiffness, etc. This
thesis addresses three technological challenges encountered when trying to achieve
the aforementioned goals, in the specific case of robot-object interaction. First,
how to overcome the inaccuracy of cable-driven systems when executing fine and
precise movements. Second, planning different tasks in dynamically changing environments.
Lastly, how the understanding of a surgical scene can be used to solve
more than one manipulation task.
To address the first challenge, a control scheme relying on accurate calibration is
implemented to execute the pick-up of a surgical needle. Regarding the planning of
surgical tasks, two approaches are explored: one is learning from demonstration to
pick and place a surgical object, and the second is using a gradient-based approach
to trigger a smoother object repositioning phase during intraoperative procedures.
Finally, to improve scene understanding, this thesis focuses on developing a simulation
environment where multiple tasks can be learned based on the surgical scene
and then transferred to the real robot. Experiments proved that automation of the pick and place task of different surgical objects is possible. The robot was successfully
able to autonomously pick up a suturing needle, position a surgical device for
intraoperative ultrasound scanning and manipulate soft tissue for intraoperative organ
retraction. Despite automation of surgical subtasks has been demonstrated in
this work, several challenges remain open, such as the capabilities of the generated
algorithm to generalise over different environment conditions and different patients
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