Neural network control of a rehabilitation robot by state and output feedback

In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control

Coordination control of robot manipulators using flat outputs

Published ArticleThis paper focuses on the synchronizing control of multiple interconnected flexible robotic manipulators using differential flatness theory. The flatness theory has the advantage of simplifying trajectory tracking tasks of complex mechanical systems. Using this theory, we propose a new synchronization scheme whereby a formation of flatness based systems can be stabilized using their respective flat outputs. Using the flat outputs, we eliminate the need for cross coupling laws and communication protocols associated with such formations. The problem of robot coordination is reduced to synchronizing the flat outputs between the respective robot manipulators. Furthermore, the selection of the flat output used for the synchronizing control is not restricted as any system variable can be used. The problem of unmeasured states used in the control is also solved by reconstructing the missing states using flatness based interpolation. The proposed control law is less computationally intensive when compared to earlier reported work as integration of the differential equations is not required. Simulations using a formation of single link flexible joint robots are used to validate the proposed synchronizing control

Coordination control of robot manipulators using flat outputs

Published ArticleThis paper focuses on the synchronizing control of multiple interconnected flexible robotic manipulators using differential flatness theory. The flatness theory has the advantage of simplifying trajectory tracking tasks of complex mechanical systems. Using this theory, we propose a new synchronization scheme whereby a formation of flatness based systems can be stabilized using their respective flat outputs. Using the flat outputs, we eliminate the need for cross coupling laws and communication protocols associated with such formations. The problem of robot coordination is reduced to synchronizing the flat outputs between the respective robot manipulators. Furthermore, the selection of the flat output used for the synchronizing control is not restricted as any system variable can be used. The problem of unmeasured states used in the control is also solved by reconstructing the missing states using flatness based interpolation. The proposed control law is less computationally intensive when compared to earlier reported work as integration of the differential equations is not required. Simulations using a formation of single link flexible joint robots are used to validate the proposed synchronizing control

Nonlinear control of multiple mobile manipulator robots transporting a rigid object in coordination

This doctoral thesis proposes and validates experimentally nonlinear control strategies for a group of mobile manipulator robots transporting a rigid object in coordination. This developed approach ensures trajectory tracking in Cartesian space in the presence of parameter uncertainty and undesirable disturbances. The objective of the creation of robots in the early sixties was to relieve man of certain hard jobs such as: handling a heavy object, and repetitive tasks which are often tiring or even sometimes infeasible manually. Following this situation, several types of manipulator robots were created. Naturally, the need for robots having both locomotion and manipulation capabilities has led to the creation of the mobile manipulators. Typical examples of mobile manipulators, more or less automated, are the cranes mounted on trucks , the satellite arms, the deep-sea exploration submarines, or extra-planetary exploration vehicles. Some operations requiring the handling of a heavy object are difficult to achieve by a single mobile manipulator. These operations require a coordination of several mobile manipulators to move or transport a heavy object in common. However, this complicates the robotic system as its control design complexity increases greatly. The problem of controlling the mechanical system forming a closed kinematic chain mechanism lies in the fact that it imposes a set of kinematic constraints on the coordination of the position and velocity of the mobile manipulator. Therefore, there is a reduction in the degrees of freedom for the entire system. Further, the internal forces of the object produced by all mobile manipulators should be controlled. This thesis work was focused on developing a consistent control technique for a group of mobile manipulator robots executing a task in coordination. Different nonlinear controllers were simulated and experimentally applied to multiple mobile manipulator system transporting a rigid object in coordination. To achieve all objectives of this thesis, as a first step, an experimental platform was developed and mounted in the laboratory of GREPCI-ETS to implement and validate the different designed control laws. In the second step, several adaptive coordinated motion/force tracking control laws were applied, ensuring that the desired trajectory can excellently tracked under uncertainties parameters and disturbances

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

Path and Motion Planning for Autonomous Mobile 3D Printing

Autonomous robotic construction was envisioned as early as the ‘90s, and yet, con- struction sites today look much alike ones half a century ago. Meanwhile, highly automated and efficient fabrication methods like Additive Manufacturing, or 3D Printing, have seen great success in conventional production. However, existing efforts to transfer printing technology to construction applications mainly rely on manufacturing-like machines and fail to utilise the capabilities of modern robotics. This thesis considers using Mobile Manipulator robots to perform large-scale Additive Manufacturing tasks. Comprised of an articulated arm and a mobile base, Mobile Manipulators, are unique in their simultaneous mobility and agility, which enables printing-in-motion, or Mobile 3D Printing. This is a 3D printing modality, where a robot deposits material along larger-than-self trajectories while in motion. Despite profound potential advantages over existing static manufacturing-like large- scale printers, Mobile 3D printing is underexplored. Therefore, this thesis tack- les Mobile 3D printing-specific challenges and proposes path and motion planning methodologies that allow this printing modality to be realised. The work details the development of Task-Consistent Path Planning that solves the problem of find- ing a valid robot-base path needed to print larger-than-self trajectories. A motion planning and control strategy is then proposed, utilising the robot-base paths found to inform an optimisation-based whole-body motion controller. Several Mobile 3D Printing robot prototypes are built throughout this work, and the overall path and motion planning strategy proposed is holistically evaluated in a series of large-scale 3D printing experiments

Predictive Context-Based Adaptive Compliance for Interaction Control of Robot Manipulators

In classical industrial robotics, robots are concealed within structured and well-known environments performing highly-repetitive tasks. In contrast, current robotic applications require more direct interaction with humans, cooperating with them to achieve a common task and entering home scenarios. Above all, robots are leaving the world of certainty to work in dynamically-changing and unstructured environments that might be partially or completely unknown to them. In such environments, controlling the interaction forces that appear when a robot contacts a certain environment (be the environment an object or a person) is of utmost importance. Common sense suggests the need to leave the stiff industrial robots and move towards compliant and adaptive robot manipulators that resemble the properties of their biological counterpart, the human arm. This thesis focuses on creating a higher level of intelligence for active compliance control methods applied to robot manipulators. This work thus proposes an architecture for compliance regulation named Predictive Context-Based Adaptive Compliance (PCAC) which is composed of three main components operating around a 'classical' impedance controller. Inspired by biological systems, the highest-level component is a Bayesian-based context predictor that allows the robot to pre-regulate the arm compliance based on predictions about the context the robot is placed in. The robot can use the information obtained while contacting the environment to update its context predictions and, in case it is necessary, to correct in real time for wrongly predicted contexts. Thus, the predictions are used both for anticipating actions to be taken 'before' proceeding with a task as well as for applying real-time corrective measures 'during' the execution of a in order to ensure a successful performance. Additionally, this thesis investigates a second component to identify the current environment among a set of known environments. This in turn allows the robot to select the proper compliance controller. The third component of the architecture presents the use of neuroevolutionary techniques for selecting the optimal parameters of the interaction controller once a certain environment has been identified

Effective robotic control through anticipating synchronisation

This thesis describes the development of a robotic control scheme with a novel ‘parallel’ design that combines direct feedback control with the simultaneous output of a dynamical forward model. Unlike existing predictive control schemes with a serial ‘sense-calculate-move’ structure where the model fully determines the robot’s behaviour, the parallel controller can adapt to overcome any feedback delay the system experiences without updating its parameters. This is thanks to replicating the key properties of anticipating synchronisation (AS), where a slave system (the robot) anticipates a similar master (a moving target) via delayed self-feedback. Since the robot and target possess very different dynamics, the output of the forward model is used to impose a suitable dynamical behaviour on the robot, while the direct feedback term simultaneously drives the robot itself to anticipate the target. This means that the forward model does not have to be related to the robot’s true dynamics so long as it represents a suitable AS slave system, and that any feedback delay will inevitably be opposed by a proportional degree of anticipation. The result is a highly robust and adaptable predictive controller that can be applied to a robot without requiring precise knowledge of its dynamics