Current Projects

Parallel Continuum Robots

Research at the intersection of parallel robots and continuum robots

Parallel continuum robots (PCR) are new mechanisms that are expected to combine dexterity and manipulability of continuum robots with accuracy and stiffness of parallel robots.

The parallel coupling of several serial-kinematic chains in parallel kinematic manipulators result in both, high dynamics and high accuracy. However, the architecture of these manipulators also has disadvantages such as a relatively small workspace and increased occurrence of singularities. On the other hand, continuum robots consist of flexible or soft materials and exhibit a high number of degrees of freedom, which leads to high dexterity and advanced motion capabilities. In contrast to conventional serial robots, their structure is very compliant, which leads to limited manipulation forces.

The underlying hypotheses of this research are:

  • increased singularity-free workspace
  • higher forces at a comparatively low weight
  • increased dexterity

Funding – German Research Foundation, 2018-2019
Collaboration with the Institute of Mechatronic System, Leibniz University Hannover, Germany (Tobias Ortmaier) Award No. BU2935/5-1

Learning the Kinematics of Tubular Continuum Robots

Model-based vs. Data-based Methods

Tubular continuum robots exhibit a curvilinear morphology which is compliant. Common robot modelling techniques to describe the relationship between actuator input in configuration space to the position and orientation in task space, and vice versa, are thus not applicable. Despite the simple actuation of component tubes, the resulting motion is characterised by a highly nonlinear behaviour due to the elastic interactions between the tubes. The current state of the art converged to approximating the curvilinear structure using continuum mechanics and formulating a set of nonlinear differential equations to solve for the quasi static shape of the robot numerically.

This model-­based approach sufficiently represents the underlying mechanics, but has limitations in terms of accuracy as well as efficiency. An inversion of the model can only be realised numerically by iteratively solving the forward model at high computational expense. High computational expense is also characterising state of the art methods in computational design optimization and motion planning, while not exploring the complete parameter space and thus leading to suboptimal results. Data­-based approaches, proposed here for the first time for tubular continuum robots, have the potential to overcome these limitations for tubular continuum robots. Deep learning can serve to discover unknown problem structures and to derive novel knowledge, which can then be used to improve and expand existing problem­ specific algorithms, but the merit is unexploited today.

The aim of this research programme is to leverage data­-based approaches and deep learning techniques for modelling, computational design, and motion planning for tubular continuum robots. The long term vision is to enable technology transfer of these techniques to real-­world applications of these robot, such as minimally invasive surgery, by focusing on the knowledge gaps for step change research. In the effort of achieving this, this research programme is planned around four objectives:

  1. Define Data Representations for Tubular Continuum Robots
  2. Investigate Deep Learning for Kinematic Modelling
  3. Enable Task-­Optimal Robot Designs by Reinforcement Learning
  4. Explore Learning­-based Motion Planning Techniques

Funding – NSERC Discovery Grant (April 1, 2019 – March 31, 2024)
NSERC Discovery Accelerator Supplements
NSERC Discovery Launch Supplements

Continuum Magnetic Robots

Improving the controllable motions of continuum robots with the addition of wireless magnetic actuation

Can a cable-driven continuum robot be controlled at multiple locations along its body using magnetic actuation?

The main limitation in existing cable-driven continuum robots is the inability to miniaturize and expand the dexterity by adding more cables at the same time. Thus, the merit of continuum robots for applications such as minimally invasive surgery or industrial inspection tasks is mostly unexploited today.

We envision a new approach to circumvent these limitations by applying wireless magnetic actuation methods from micro-scale robotics research to yield a robot significantly more dexterous and functional than existing continuum robots today. We will develop the mathematical models for such a novel system and build a first-ever prototype magnetic continuum robot.

This project is a collaboration between CRL and the Microrobotics Lab (Eric Diller).

Funding – XSeed Fund, September 1, 2019 to August 31, 2021
XSeed is a seed funding program of the Faculty of Applied Science & Engineering and the Faculty of Arts and Science at University of Toronto to promote interdisciplinary research and to catalyze innovative partnerships.

MEDUSA - Single Port Continuum Robot System

Enhancing dexterity in minimally invasive surgery through a single inciscion

We are leveraging continuum robotics to create a flexible variable stiffness endoport for laparoscopic single-site surgery. The endoport concept is a flexible manipulator realized as a two-segment tendon-actuated continuum robot capable of stiffening its structure through a layer jamming sheath. Three working channels offer application and quick exchange of flexible surgical tools, either manual or robotic as well as endoscopes.

Past Projects

CROSS - Continuum Robots for Surgical Systems

Designing and controlling concentric tube continuum robots in humans.

In CROSS we were leveraging concentric tube continuum robots in surgical systems. Our research focused on computational design optimization, motion planning algorithms as well as system design and human robot interaction. Potential medical applications include endonasal skull base surgery, transurethral kidney stone removal, and transcranial hemorrhage treatment.0

Funding – German Research Foundation, 2013-2019
Emmy Noether Independent Junior Research Group Award No. BU2935/1-1