Research

Small-sized continuum robots (CRs) have shown promising applications in multiple fields, including minimally invasive surgeries. This work contributes to the field by presenting dynamic modeling and system identification of internally actuated, small-sized CRs in which continuous interactions between the internal actuation mechanisms and the flexible backbones are considered.

First, a dynamic model of a flexible backbone sheath is developed. Next, an equivalent discrete model is identified to simulate the dynamics of CRs with continuous interactions. Moreover, based on the existing friction models of passive tendon-sheath mechanisms, the friction model within the proposed discrete model is modified to better resemble the continuity of the interaction. A prismatic ablation catheter is then used as the benchmark for the experimental study, in which parameters for the handle, shaft, and the proposed discrete model for its bending section are considered unknown. The identified model estimates the catheter motion with a maximum error of 4.5 mm at the tip position, which represents 5.6% of the length of the bending section of our experimental catheter.

Despite the importance of grasp synthesis for autonomous robotic operations, its formulation for continuum robots (CRs) has yet to be investigated. This work presents the first grasp taxonomy and a synthesis approach for CRs. The proposed synthesis relies on an analytical model for grasping using CRs. The method is extended for the application of cooperative continuum robots (CCRs).

To present a comprehensive formulation of the problem, both constant-curvature and Cosserat-rod models are adopted in the proposed grasp synthesis. A set of grasp quality measures are formulated for this study, in which a new quantitative grasp quality measure is introduced to reflect the limited workspace of CRs. Finally, two experimental grasp quality measures are introduced (i.e., path-following error and grasp success rate) to compare and assess the grasps using statistical tests. The effectiveness of the proposed methodology is shown through extensive simulations and experiments using single-segment tendon-driven catheters.

This investigation focuses on servoing a robot's tool with respect to a smooth workpiece surface by making use of the surface's characteristic local differential properties. A novel formulation for 6 degree-of-freedom (DoF) textureless visual servoing based on these properties is proposed, which extends an existing 3-DoF scheme.

Our approach naturally combines the geometric tools of computer-aided design and machining (CAD/CAM) theory with the manipulator control tools of visual servoing synergistically to achieve full 6-DoF pose control. A novel family of observed feature sets and their associated interaction matrices are presented. A geometric condition on the surface shape is derived under which local asymptotic stability for 6-DoF is guaranteed. Validation of the proposed method is performed in simulation and experiment using an articulated desktop robot equipped with only a monocular camera and 16 laser pointers.

Due to their compliance, continuum robots (CRs) hold great potential for many applications. However, despite intensive recent research, their control poses significant challenges. Nonlinear kinematic behavior, limited actuation channels, and physical and environmental constraints, typically associated with CRs, hinder the development of effective control strategies.

In this project, a visual predictive position control method for tendon-driven continuum robots is proposed. The developed control approach integrates the advantages of image-based visual servoing and model predictive control techniques to enable direct end-point control in the presence of constraints, and improve the control robustness to system uncertainties, sensing noise, and modeling errors. Both simulation and experimental results demonstrate the effectiveness of the method.

Visual servoing methods have proven their usefulness for robot control in unstructured environments. However, the practicality of such methods highly depends on their robustness to system uncertainties and ability to handle the constraints of the system. Many of the previous works proposed effective remedies for constraint handling; yet, only a few of them considered the system uncertainties.

This work proposes a novel control scheme for visual servoing which basically exploits a model predictive controller to handle the constraints such as Cartesian obstacles. In addition to that, the uncertainty model of the system is developed to handle the constraints more efficiently. The simulation and experimental results confirm the effectiveness of the proposed control method for constraint handling, in the presence of system uncertainties and constraints.

Part of our on-going research in the laboratory is to develop robust and practical visual servo control systems for the robots. A novel model predictive control scheme for constrained visual servoing is presented here.

The proposed method compensates the shortcomings of available MPC schemes in visual servoing and can be utilized for positioning robots in uncertain environments with internal and external constrains. The system model is designed by weighted conjugating of the well-known image-based and position-based approaches. The stability analysis of the control scheme is presented and by illustrating several simulations, the performance and robustness of proposed control structure is demonstrated.

As a part of our on-going effort to develop robust and practical robot visual servo control systems, we have developed a depth-based visual predictive control. This work presents a novel depth-based visual predictive control (DVPC) method to overcome several shortcomings of the previous IBVS methods.

In particular, the proposed approach enables constraints enforcement and addresses one of the critical issues of IBVS method, namely camera retreat problem. Furthermore, the proposed approach is applied for the control of continuum robots (CR). Simulation results are presented to verify the efficiency and functionality of the proposed approach.

Visual servoing methods have proven their merits for robot control in unstructured environments. However, many applications require their robustness to system uncertainties and ability to handle system constraints. Many of the previous works proposed effective remedies for constraint handling; yet, only a few of them considered the system uncertainties. In this work, a new visual predictive control framework for handling uncertainties and constraints is proposed.

A stochastic image-based visual predictive control method is proposed to overcome some shortcomings of the previous schemes. In particular, the proposed approach provides a systematic solution to address the image-based constraint compliance in presence of the measurement and modelling uncertainties. The proposed method was implemented on a 6-DOF Denso robot via simulation.

Robot-assisted catheterization systems (RACS) have been introduced in recent years to improve the efficiency of CBIs; however, using them is still associated with some difficulties such as set-up dependency to a specific type of intervention instrument, not being portable, and offering limited options of operation modes. The objective of this research is to develop a new RACS to address these shortcomings.

We have developed Althea II as an improvement for our previously introduced RACS, Althea I. Althea II is designed for both research purposes and clinical applications including catheter-based cardiovascular interventions. Althea II benefits from a novel structural design leading to a significantly reduced weight and making the device inclusive for a broader range of intervention instruments. The preliminary studies verified the accuracy and repeatability of Althea II, demonstrated the feasibility and applicability of using it in multiple applications, and highlighted the improved set-up capabilities over the currently available RACS.

The main objective of this project is to develop an efficient method for learning and reproduction of complex trajectories for robot programming by demonstration.

Encoding of the demonstrated trajectories is performed with hidden Markov model, and generation of a generalized trajectory is achieved by using the concept of key points. Identification of the key points is based on significant changes in position and velocity in the demonstrated trajectories. The resulting sequences of trajectory key points are temporally aligned using the multidimensional dynamic time warping algorithm, and a generalized trajectory is obtained by smoothing spline interpolation of the clustered key points.

The principal advantage of our proposed approach is utilization of the trajectory key points from all demonstrations for generation of a generalized trajectory. In addition, variability of the key points' clusters across the demonstrated set is employed for assigning weighting coefficients, resulting in a generalization procedure which accounts for the relevance of reproduction of different parts of the trajectories. The approach is verified experimentally for trajectories with two different levels of complexity.

An image-based trajectory programming technology has also been patented.

Vision-based surface coverage measurement serves a large area of applications including robotic shot peening, robotic inspection in the fields of blood cell segmentation, coin recognition systems and many more.

Manual visual inspection is time consuming and prone to human error. The objective of this project is to develop a real-time generic algorithm, robust to non-uniform illumination with high accuracy, and having a relatively simple experimental setup.

In this work, a hybrid method based on morphological operations and Boykov graph-cuts segmentation is proposed for vision-based surface coverage measurement. While most of the techniques are accurate in segmenting the shot-peened areas, they seem to fail in the presence of machining streaks, resulting in false segmentation.

To overcome this challenge, an artificial neural network (ANN)-based implementation is also designed to improve accuracy of the results. The neural network is trained with specific selected features from the acquired images. Results show ANN outperforms the previously implemented standard image segmentation methods.

Automatic surface paint coverage measurement is highly desired by the industry for its potential accuracy and promising efficiency.

In this project, we are developing vision-based real-time surface paint measurement using artificial neural networks and graph-cut techniques. The approach is also benefiting from robotic technology for automatic navigation and measurement of surfaces.

The aim of this project was to track flying objects in sky and classify them. Several applications could benefit from the approach including high-security premises such as airports, armed force bases, etc.