Trajectory Generation for Robotic Applications using Point Cloud Data

Throughout this project, the aim is to develop a program that, based on the processing of three-dimensional point clouds belonging to objects or shapes, achieves the creation of trajectories through route generation algorithms. These trajectories can be subsequently followed by a manipulator robot. The motivation for this project arises as a contribution to the current development of industrial applications involving robotics, where the use of computer vision techniques is increasingly relevant. This implementation can be beneficial in industrial settings such as welding of metal parts, where trajectory planning along the object's surface is required

An Overview of Industrial Robots Control and Programming Approaches

Nowadays, manufacturing plants are required to be flexible to respond quickly to customer demands, adapting production and processes without affecting their efficiency. In this context, Industrial Robots (IRs) are a primary resource for modern factories due to their versatility which allows the execution of flexible, reconfigurable, and zero-defect manufacturing tasks. Even so, the control and programming of the commercially available IRs are limiting factors for their effective implementation, especially for dynamic production environments or when complex applications are required. These issues have stimulated the development of new technologies that support more efficient methods for robot control and programming. The goal of this research is to identify and evaluate the main approaches proposed in scientific papers and by the robotics industry in the last decades. After a critical review of the standard IR control schematic, the paper discusses the available control alternatives and summarizes their characteristics, range of applications, and remaining limitations

Improving the safety and efficiency of rail yard operations using robotics

textSignificant efforts have been expended by the railroad industry to make operations safer and more efficient through the intelligent use of sensor data. This work proposes to take the technology one step further to use this data for the control of physical systems designed to automate hazardous railroad operations, particularly those that require humans to interact with moving trains. To accomplish this, application specific requirements must be established to design self-contained machine vision and robotic solutions to eliminate the risks associated with existing manual operations. Present-day rail yard operations have been identified as good candidates to begin development. Manual uncoupling, in particular, of rolling stock in classification yards has been investigated. To automate this process, an intelligent robotic system must be able to detect, track, approach, contact, and manipulate constrained objects on equipment in motion. This work presents multiple prototypes capable of autonomously uncoupling full-scale freight cars using feedback from its surrounding environment. Geometric image processing algorithms and machine learning techniques were implemented to accurately identify cylindrical objects in point clouds generated in real-vi time. Unique methods fusing velocity and vision data were developed to synchronize a pair of moving rigid bodies in real-time. Multiple custom end-effectors with in-built compliance and fault tolerance were designed, fabricated, and tested for grasping and manipulating cylindrical objects. Finally, an event-driven robotic control application was developed to safely and reliably uncouple freight cars using data from 3D cameras, velocity sensors, force/torque transducers, and intelligent end-effector tooling. Experimental results in a lab setting confirm that modern robotic and sensing hardware can be used to reliably separate pairs of rolling stock up to two miles per hour. Additionally, subcomponents of the autonomous pin-pulling system (APPS) were designed to be modular to the point where they could be used to automate other hazardous, labor-intensive tasks found in U.S. classification yards. Overall, this work supports the deployment of autonomous robotic systems in semi-unstructured yard environments to increase the safety and efficiency of rail operations.Mechanical Engineerin

Force-controlled Transcranial Magnetic Stimulation (TMS) robotic system

The use of robots to assist neurologists in Transcranial Magnetic Stimulation (TMS) has the potential to improve the long term outcome of brain stimulation. Although extensive research has been carried out on TMS robotic system, no single study exists which adequately take into account the control of interaction of contact force between the robot and subject’s head. Thus, the introduction of force feedback control is considered as a desirable feature, and is particularly important when using an autonomous robot manipulator. In this study, a force-controlled TMS robotic system has been developed, which consists of a 6 degree of freedom (DOF) articulated robot arm, a force/torque sensor system to measure contact force and real-time PC based control system. A variant of the external force control scheme was successfully implemented to carry out the simultaneous force and position control in real-time. A number of engineering challenges are addressed to develop a viable system for TMS application; simultaneous real-time force and position tracking on subject’s head, unknown/varies environment stiffness and motion compensation to counter the force-controlled instability problems, and safe automated robotic system. Simulation of a single axis force-controlled robotic system has been carried out, which includes a task of maintaining contact on simulated subject’s head. The results provide a good agreement with parallel experimental tests, which leads to further improvement to the robot force control. An Adaptive Neuro-Fuzzy Force Controller has been developed to provide stable and robust force control on unknown environment stiffness and motion. The potential of the proposed method has been further illustrated and verified through a comprehensive series of experiments. This work also lays important foundations for long term related research, particularly in the development of real-time medical robotic system and new techniques of force control mainly for human-robot interaction. KEY WORDS: Transcranial Magnetic Stimulation, Robotic System, Real-time System, External Force Control Scheme, Adaptive Neuro-Fuzzy Force ControllerEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Force-controlled Transcranial Magnetic Stimulation (TMS) robotic system

The use of robots to assist neurologists in Transcranial Magnetic Stimulation (TMS) has the potential to improve the long term outcome of brain stimulation. Although extensive research has been carried out on TMS robotic system, no single study exists which adequately take into account the control of interaction of contact force between the robot and subject’s head. Thus, the introduction of force feedback control is considered as a desirable feature, and is particularly important when using an autonomous robot manipulator. In this study, a force-controlled TMS robotic system has been developed, which consists of a 6 degree of freedom (DOF) articulated robot arm, a force/torque sensor system to measure contact force and real-time PC based control system. A variant of the external force control scheme was successfully implemented to carry out the simultaneous force and position control in real-time. A number of engineering challenges are addressed to develop a viable system for TMS application; simultaneous real-time force and position tracking on subject’s head, unknown/varies environment stiffness and motion compensation to counter the force-controlled instability problems, and safe automated robotic system. Simulation of a single axis force-controlled robotic system has been carried out, which includes a task of maintaining contact on simulated subject’s head. The results provide a good agreement with parallel experimental tests, which leads to further improvement to the robot force control. An Adaptive Neuro-Fuzzy Force Controller has been developed to provide stable and robust force control on unknown environment stiffness and motion. The potential of the proposed method has been further illustrated and verified through a comprehensive series of experiments. This work also lays important foundations for long term related research, particularly in the development of real-time medical robotic system and new techniques of force control mainly for human-robot interaction. KEY WORDS: Transcranial Magnetic Stimulation, Robotic System, Real-time System, External Force Control Scheme, Adaptive Neuro-Fuzzy Force ControllerEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Exploiting Heterogeneity in Networks of Aerial and Ground Robotic Agents

By taking advantage of complementary communication technologies, distinct sensing functionalities and varied motion dynamics present in a heterogeneous multi-robotic network, it is possible to accomplish a main mission objective by assigning specialized sub-tasks to specific members of a robotic team. An adequate selection of the team members and an effective coordination are some of the challenges to fully exploit the unique capabilities that these types of systems can offer. Motivated by real world applications, we focus on a multi-robotic network consisting off aerial and ground agents which has the potential to provide critical support to humans in complex settings. For instance, aerial robotic relays are capable of transporting small ground mobile sensors to expand the communication range and the situational awareness of first responders in hazardous environments. In the first part of this dissertation, we extend work on manipulation of cable-suspended loads using aerial robots by solving the problem of lifting the cable-suspended load from the ground before proceeding to transport it. Since the suspended load-quadrotor system experiences switching conditions during this critical maneuver, we define a hybrid system and show that it is differentially-flat. This property facilitates the design of a nonlinear controller which tracks a waypoint-based trajectory associated with the discrete states of the hybrid system. In addition, we address the case of unknown payload mass by combining a least-squares estimation method with the designed controller. Second, we focus on the coordination of a heterogeneous team formed by a group of ground mobile sensors and a flying communication router which is deployed to sense areas of interest in a cluttered environment. Using potential field methods, we propose a controller for the coordinated mobility of the team to guarantee inter-robot and obstacle collision avoidance as well as connectivity maintenance among the ground agents while the main goal of sensing is carried out. For the case of the aerial communications relays, we combine antenna diversity with reinforcement learning to dynamically re-locate these relays so that the received signal strength is maintained above a desired threshold. Motivated by the recent interest of combining radio frequency and optical wireless communications, we envision the implementation of an optical link between micro-scale aerial and ground robots. This type of link requires maintaining a sufficient relative transmitter-receiver position for reliable communications. In the third part of this thesis, we tackle this problem. Based on the link model, we define a connectivity cone where a minimum transmission rate is guaranteed. For example, the aerial robot has to track the ground vehicle to stay inside this cone. The control must be robust to noisy measurements. Thus, we use particle filters to obtain a better estimation of the receiver position and we design a control algorithm for the flying robot to enhance the transmission rate. Also, we consider the problem of pairing a ground sensor with an aerial vehicle, both equipped with a hybrid radio-frequency/optical wireless communication system. A challenge is positioning the flying robot within optical range when the sensor location is unknown. Thus, we take advantage of the hybrid communication scheme by developing a control strategy that uses the radio signal to guide the aerial platform to the ground sensor. Once the optical-based signal strength has achieved a certain threshold, the robot hovers within optical range. Finally, we investigate the problem of building an alliance of agents with different skills in order to satisfy the requirements imposed by a given task. We find this alliance, known also as a coalition, by using a bipartite graph in which edges represent the relation between agent capabilities and required resources for task execution. Using this graph, we build a coalition whose total capability resources can satisfy the task resource requirements. Also, we study the heterogeneity of the formed coalition to analyze how it is affected for instance by the amount of capability resources present in the agents

MRI compatible mechatronic devices to aid medical diagnosis and intervention

Imperial Users onl

Soft Robotics: Design for Simplicity, Performance, and Robustness of Robots for Interaction with Humans.

This thesis deals with the design possibilities concerning the next generation of advanced Robots. Aim of the work is to study, analyse and realise artificial systems that are essentially simple, performing and robust and can live and coexist with humans. The main design guideline followed in doing so is the Soft Robotics Approach, that implies the design of systems with intrinsic mechanical compliance in their architecture. The first part of the thesis addresses design of new soft robotics actuators, or robotic muscles. At the beginning are provided information about what a robotic muscle is and what is needed to realise it. A possible classification of these systems is analysed and some criteria useful for their comparison are explained. After, a set of functional specifications and parameters is identified and defined, to characterise a specific subset of this kind of actuators, called Variable Stiffness Actuators. The selected parameters converge in a data-sheet that easily defines performance and abilities of the robotic system. A complete strategy for the design and realisation of this kind of system is provided, which takes into account their me- chanical morphology and architecture. As consequence of this, some new actuators are developed, validated and employed in the execution of complex experimental tasks. In particular the actuator VSA-Cube and its add-on, a Variable Damper, are developed as the main com- ponents of a robotics low-cost platform, called VSA-CubeBot, that v can be used as an exploratory platform for multi degrees of freedom experiments. Experimental validations and mathematical models of the system employed in multi degrees of freedom tasks (bimanual as- sembly and drawing on an uneven surface), are reported. The second part of the thesis is about the design of multi fingered hands for robots. In this part of the work the Pisa-IIT SoftHand is introduced. It is a novel robot hand prototype designed with the purpose of being as easily usable, robust and simple as an industrial gripper, while exhibiting a level of grasping versatility and an aspect comparable to that of the human hand. In the thesis the main theo- retical tool used to enable such simplification, i.e. the neuroscience– based notion of soft synergies, are briefly reviewed. The approach proposed rests on ideas coming from underactuated hand design. A synthesis method to realize a desired set of soft synergies through the principled design of adaptive underactuated mechanisms, which is called the method of adaptive synergies, is discussed. This ap- proach leads to the design of hands accommodating in principle an arbitrary number of soft synergies, as demonstrated in grasping and manipulation simulations and experiments with a prototype. As a particular instance of application of the method of adaptive syner- gies, the Pisa–IIT SoftHand is then described in detail. The design and implementation of the prototype hand are shown and its effec- tiveness demonstrated through grasping experiments. Finally, control of the Pisa/IIT Hand is considered. Few different control strategies are adopted, including an experimental setup with the use of surface Electromyographic signals