Design and Control of a Multiple-Section Continuum Robot With a Hybrid Sensing System

After decades of development, the technology of continuum robots continues to mature gradually and is used for various applications, e.g., inspection and repair in high-value-added industries such as aerospace & nuclear. However, such robots are invariably designed with a hyper-redundant structure which causes condition-dependent uncertainties and errors in practical operations. In this work, a hybrid sensing solution-consisting of a dual displacement sensor system (linear & rotary encoders) and a self-carried visual tracking system-is presented to improve the control performance of a multi-section cable-driven continuum robot. The dual displacement sensor system works to measure the cable-displacement error at the proximal end of the arm and predicts the cable elongation in the arm to eliminate the control error of the robot in joint-space via a new two-stage cable-displacement control approach. Simultaneously, the self-carried visual system provides real-time section-by-section tip tracking to improve the controllability of the arm in the task space. This feedback is then integrated with a two-level closed-loop controller to achieve accurate tip-positioning control. A series of validation experiments are carried out to validate the approach. Compared with an off-the-shelf position tracking system (VICON), the measurement error of the proposed self-carried visual tracking system is smaller than 2 mm, with a mean error of 0.67 mm, within the work-space of a single-section continuum robot. The distal-tip control accuracy of a 2-section robot can achieve 1.72 mm under the closed-loop controller supported by the hybrid sensing system

Developing Intuitive, Closed-Loop, Teleoperative Control of Continuum Robotic Systems

This thesis presents a series of related new results in the area of continuum robot teleoperation and control. A new nonlinear control strategy for the teleoperation of extensible continuum robots is described. Previous attempts at controlling continuum robots have proven difficult due to the complexity of their system dynamics. Taking advantage of a previously developed dynamic model for a three-section, planar, continuum manipulator, we present an adaptation control-inspired law. Simulation and experimental results of a teleoperation scheme between a master device and an extensible continuum slave manipulator using the new controller are presented. Two novel user interface approaches to the teleoperation of continuum robots are also presented. In the first, mappings from a six Degree-of-Freedom (DoF) rigid-link robotic arm to a nine degree-of-freedom continuum robot are synthesized, analyzed, and implemented, focusing on their potential for creating an intuitive operational interface. Tests were conducted across a range of planar and spatial tasks, using fifteen participant operators. The results demonstrate the feasibility of the approach, and suggest that it can be effective independent of the prior robotics, gaming, or teleoperative experience of the operator. In the second teleoperation approach, a novel nine degree-of-freedom input device for the teleoperation of extensible continuum robots is introduced. As opposed to previous works limited by kinematically dissimilar master devices or restricted degrees-of-freedom, the device is capable of achieving configurations identical to a three section continuum robot, and simplifying the control of such manipulators. The thesis discusses the design of the control device and its construction. The implementation of the new master device is discussed and the effectiveness of the system is reported

Nonlinear Modeling and Control of Driving Interfaces and Continuum Robots for System Performance Gains

With the rise of (semi)autonomous vehicles and continuum robotics technology and applications, there has been an increasing interest in controller and haptic interface designs. The presence of nonlinearities in the vehicle dynamics is the main challenge in the selection of control algorithms for real-time regulation and tracking of (semi)autonomous vehicles. Moreover, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics plus the soft and flexible nature of the manipulator body. The trajectory tracking and control of automobile and robotic systems requires control algorithms that can effectively deal with the nonlinearities of the system without the need for approximation, modeling uncertainties, and input disturbances. Control strategies based on a linearized model are often inadequate in meeting precise performance requirements. To cope with these challenges, one must consider nonlinear techniques. Nonlinear control systems provide tools and methodologies for enabling the design and realization of (semi)autonomous vehicle and continuum robots with extended specifications based on the operational mission profiles. This dissertation provides an insight into various nonlinear controllers developed for (semi)autonomous vehicles and continuum robots as a guideline for future applications in the automobile and soft robotics field. A comprehensive assessment of the approaches and control strategies, as well as insight into the future areas of research in this field, are presented.First, two vehicle haptic interfaces, including a robotic grip and a joystick, both of which are accompanied by nonlinear sliding mode control, have been developed and studied on a steer-by-wire platform integrated with a virtual reality driving environment. An operator-in-the-loop evaluation that included 30 human test subjects was used to investigate these haptic steering interfaces over a prescribed series of driving maneuvers through real time data logging and post-test questionnaires. A conventional steering wheel with a robust sliding mode controller was used for all the driving events for comparison. Test subjects operated these interfaces for a given track comprised of a double lane-change maneuver and a country road driving event. Subjective and objective results demonstrate that the driver’s experience can be enhanced up to 75.3% with a robotic steering input when compared to the traditional steering wheel during extreme maneuvers such as high-speed driving and sharp turn (e.g., hairpin turn) passing. Second, a cellphone-inspired portable human-machine-interface (HMI) that incorporated the directional control of the vehicle as well as the brake and throttle functionality into a single holistic device will be presented. A nonlinear adaptive control technique and an optimal control approach based on driver intent were also proposed to accompany the mechatronic system for combined longitudinal and lateral vehicle guidance. Assisting the disabled drivers by excluding extensive arm and leg movements ergonomically, the device has been tested in a driving simulator platform. Human test subjects evaluated the mechatronic system with various control configurations through obstacle avoidance and city road driving test, and a conventional set of steering wheel and pedals were also utilized for comparison. Subjective and objective results from the tests demonstrate that the mobile driving interface with the proposed control scheme can enhance the driver’s performance by up to 55.8% when compared to the traditional driving system during aggressive maneuvers. The system’s superior performance during certain vehicle maneuvers and approval received from the participants demonstrated its potential as an alternative driving adaptation for disabled drivers. Third, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which inverse kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is validated in a realistic simulation and implemented on a six degree-of-freedom two-section OctArm continuum manipulator. Both simulation and experimental results show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent tracking performance (e.g. steady state arc length and curvature tracking error of 3.3mm and 130mm-1, respectively). Last, semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. The proposed holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer vehicle operations

Collaborative system and multi robots based on pneumatic muscle actuator

Designing a multi-robot system provides numerous advantages for many applications, such as low cost, multi-tasking and more efficient group work. While the rigidity of the robots used in industrial and medical application increase the probability of risk of injury. Therefore, many researches are done to increase the safety factor for robot-human interaction, as a result, either the separated between the human and robot is suggested or the force shutdown to robot system is applied. These solutions might be useful for industrial applications, nonetheless it is not for medical and the application require the direct interaction between the human and machine. To overcome the rigidity problem, a soft pneumatic muscle actuator PMA is used in this thesis to design a fully soft robot arm.The performances and the behaviours of these actuators are tested to enhance the force formula for the contraction and the extension PMAs. General length formulas are proposed in terms of the initial length in addition to the structure-based formulas for the tensile force and length. Three different novel actuators are proposed together with their kinematics. These actuators include: the self-bending contraction actuator SBCA, the double-bend pneumatic muscle actuator DB-PMA and the circular pneumatic muscle actuator CPMA. The presented actuators are used with the simple contraction and extension actuators to design different novel structures of continuum arms and end effectors. Then an efficient control system is proposed by using a parallel structure of the neural network NN and proportional P controller (PNNP controller). The presented continuum arms formed a multiple robot system to perform several tasks under the PNNP controller