Motion control design for unmanned ground vehicle in dynamic environment using intelligent controller

The motion control of unmanned ground vehicles is essential in the industry of automation. In this paper, the sensors of a fuzzy inference system that is based on a navigation technique for an unmanned ground vehicle are formulated in a cluttered dynamic environment. This fuzzy inference system consists of two controllers. The first controller uses three sensors based on the distances from the front, the right and the left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to choose the optimal route based on the dynamic environment and reach the desired destination with minimum running power and time. Experimental tests have been carried out in three different case studies to investigate the validation and effectiveness of the introduced controllers of the fuzzy inference system. The reported simulation results are conducted using MATLAB software package. The results show that the controllers of the fuzzy inference system consistently perform the maneuvering task and route planning efficiently even in a complex environment with populated dynamic obstacles

Design and Development of an Automated Mobile Manipulator for Industrial Applications

This thesis presents the modeling, control and coordination of an automated mobile manipulator. A mobile manipulator in this investigation consists of a robotic manipulator and a mobile platform resulting in a hybrid mechanism that includes a mobile platform for locomotion and a manipulator arm for manipulation. The structural complexity of a mobile manipulator is the main challenging issue because it includes several problems like adapting a manipulator and a redundancy mobile platform at non-holonomic constraints. The objective of the thesis is to fabricate an automated mobile manipulator and develop control algorithms that effectively coordinate the arm manipulation and mobility of mobile platform. The research work starts with deriving the motion equations of mobile manipulators. The derivation introduced here makes use of motion equations of robot manipulators and mobile platforms separately, and then integrated them as one entity. The kinematic analysis is performed in two ways namely forward & inverse kinematics. The motion analysis is performed for various WMPs such as, Omnidirectional WMP, Differential three WMP, Three wheeled omni-steer WMP, Tricycle WMP and Two steer WMP. From the obtained motion analysis results, Differential three WMP is chosen as the mobile platform for the developed mobile manipulator. Later motion analysis is carried out for 4-axis articulated arm. Danvit-Hartenberg representation is implemented to perform forward kinematic analysis. Because of this representation, one can easily understand the kinematic equation for a robotic arm. From the obtained arm equation, Inverse kinematic model for the 4-axis robotic manipulator is developed. Motion planning of an intelligent mobile robot is one of the most vital issues in the field of robotics, which includes the generation of optimal collision free trajectories within its work space and finally reaches its target position. For solving this problem, two evolutionary algorithms namely Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) are introduced to move the mobile platform in intelligent manner. The developed algorithms are effective in avoiding obstacles, trap situations and generating optimal paths within its unknown environments. Once the robot reaches its goal (within the work space of the manipulator), the manipulator will generate its trajectories according to task assigned by the user. Simulation analyses are performed using MATLAB-2010 in order to validate the feasibility of the developed methodologies in various unknown environments. Additionally, experiments are carried out on an automated mobile manipulator. ATmega16 Microcontrollers are used to enable the entire robot system movement in desired trajectories by means of robot interface application program. The control program is developed in robot software (Keil) to control the mobile manipulator servomotors via a serial connection through a personal computer. To support the proposed control algorithms both simulation and experimental results are presented. Moreover, validation of the developed methodologies has been made with the ER-400 mobile platform

Closed-Loop Planning and Control of Steerable Medical Needles

Steerable needles have the potential to increase the effectiveness of needle-based clinical procedures such as biopsy, drug delivery, and radioactive seed implantation for cancer treatment. These needles can trace curved paths when inserted into tissue, thereby increasing maneuverability and targeting accuracy while reaching previously inaccessible targets that are behind sensitive or impenetrable anatomical regions. Guiding these flexible needles along an intended path requires continuously inserting and twisting the needle at its base, which is not intuitive for a human operator. In addition, the needle often deviates from its intended trajectory due to factors such as tissue deformation, needle-tissue interaction, noisy actuation and sensing, modeling errors, and involuntary patient motions. These challenges can be addressed with the assistance of robotic systems that automatically compensate for these perturbations by performing motion planning and feedback control of the needle in a closed-loop fashion under sensory feedback. We present two approaches for efficient closed-loop guidance of steerable needles to targets within clinically acceptable accuracy while safely avoiding sensitive or impenetrable anatomical structures. The first approach uses a fast motion planning algorithm that unifies planning and control by continuously replanning, enabling correction for perturbations as they occur. We evaluate our method using a needle steering system in phantom and ex vivo animal tissues. The second approach integrates motion planning and feedback control of steerable needles in highly deformable environments. We demonstrate that this approach significantly improves the probability of success compared to prior approaches that either consider uncertainty or deformations but not both simultaneously. We also propose a data-driven method to estimate parameters of stochastic models of steerable needle motion. These models can be used to create realistic medical simulators for clinicians wanting to train for steerable needle procedures and to improve the effectiveness of existing planning and control methods. This dissertation advances the state of the art in planning and control of steerable needles and is an important step towards realizing needle steering in clinical practice. The methods developed in this dissertation also generalize to important applications beyond medical needle steering, such as manipulating deformable objects and control of mobile robots.Doctor of Philosoph

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

Modeling and Control of Steerable Ablation Catheters

Catheters are long, flexible tubes that are extensively used in vascular and cardiac interventions, e.g., cardiac ablation, coronary angiography and mitral valve annuloplasty. Catheter-based cardiac ablation is a well-accepted treatment for atrial fibrillation, a common type of cardiac arrhythmia. During this procedure, a steerable ablation catheter is guided through the vasculature to the left atrium to correct the signal pathways inside the heart and restore normal heart rhythm. The outcome of the ablation procedure depends mainly on the correct positioning of the catheter tip at the target location inside the heart and also on maintaining a consistent contact between the catheter tip and cardiac tissue. In the presence of cardiac and respiratory motions, achieving these goals during the ablation procedure is very challenging without proper 3D visualization, dexterous control of the flexible catheter and an estimate of the catheter tip/tissue contact force. This research project provides the required basis for developing a robotics-assisted catheter manipulation system with contact force control for use in cardiac ablation procedures. The behavior of the catheter is studied in free space as well in contact with the environment to develop mathematical models of the catheter tip that are well suited for developing control systems. The validity of the proposed modeling approaches and the performance of the suggested control techniques are evaluated experimentally. As the first step, the static force-deflection relationship for ablation catheters is described with a large-deflection beam model and an optimized pseudo-rigid-body 3R model. The proposed static model is then used in developing a control system for controlling the contact force when the catheter tip is interacting with a static environment. Our studies also showed that it is possible to estimate the tip/tissue contact force by analyzing the shape of the catheter without installing a force sensor on the catheter. During cardiac ablation, the catheter tip is in contact with a relatively fast moving environment (cardiac tissue). Robotic manipulation of the catheter has the potential to improve the quality of contact between the catheter tip and cardiac tissue. To this end, the frequency response of the catheter is investigated and a control technique is proposed to compensate for the cardiac motion and to maintain a constant tip/tissue contact force. Our study on developing a motion compensated robotics-assisted catheter manipulation system suggests that redesigning the actuation mechanism of current ablation catheters would provide a major improvement in using these catheters in robotics-assisted cardiac ablation procedures

Towards Robot Autonomy in Medical Procedures Via Visual Localization and Motion Planning

Robots performing medical procedures with autonomous capabilities have the potential to positively effect patient care and healthcare system efficiency. These benefits can be realized by autonomous robots facilitating novel procedures, increasing operative efficiency, standardizing intra- and inter-physician performance, democratizing specialized care, and focusing the physician’s time on subtasks that best leverage their expertise. However, enabling medical robots to act autonomously in a procedural environment is extremely challenging. The deforming and unstructured nature of the environment, the lack of features in the anatomy, and sensor size constraints coupled with the millimeter level accuracy required for safe medical procedures introduce a host of challenges not faced by robots operating in structured environments such as factories or warehouses. Robot motion planning and localization are two fundamental abilities for enabling robot autonomy. Motion planning methods compute a sequence of safe and feasible motions for a robot to accomplish a specified task, where safe and feasible are defined by constraints with respect to the robot and its environment. Localization methods estimate the position and orientation of a robot in its environment. Developing such methods for medical robots that overcome the unique challenges in procedural environments is critical for enabling medical robot autonomy. In this dissertation, I developed and evaluated motion planning and localization algorithms towards robot autonomy in medical procedures. A majority of my work was done in the context of an autonomous medical robot built for enhanced lung nodule biopsy. First, I developed a dataset of medical environments spanning various organs and procedures to foster future research into medical robots and automation. I used this data in my own work described throughout this dissertation. Next, I used motion planning to characterize the capabilities of the lung nodule biopsy robot compared to existing clinical tools and I highlighted trade-offs in robot design considerations. Then, I conducted a study to experimentally demonstrate the benefits of the autonomous lung robot in accessing otherwise hard-to-reach lung nodules. I showed that the robot enables access to lung regions beyond the reach of existing clinical tools with millimeter-level accuracy sufficient for accessing the smallest clinically operable nodules. Next, I developed a localization method to estimate the bronchoscope’s position and orientation in the airways with respect to a preoperatively planned needle insertion pose. The method can be used by robotic bronchoscopy systems and by traditional manually navigated bronchoscopes. The method is designed to overcome challenges with tissue motion and visual homogeneity in the airways. I demonstrated the success of this method in simulated lungs undergoing respiratory motion and showed the method’s ability to generalize across patients.Doctor of Philosoph