TASK-BASED OPTIMIZATION OF MULTI-ARM SPACE ROBOTICS

There are many benefits to using multi-arm systems over a single arm system including higher flexibility in planning, better payload handling capacity, and reduction of joint torques. However, multi-arm systems are inherently more complex. This complexity does not necessarily translate to ``bigger" and ``heavier". This research seeks to answer the question of whether or not a multi-arm system can have lower mass than a single arm system. Using a task-based methodology, Independent single-arm and cooperative dual-arm manipulator systems are designed. A task defines the payload's motion and thus the manipulator's trajectory. Utilizing linear programming, a new method is developed in order to optimize the distribution of forces among the multiple arms in order to guarantee a minimum system mass. The mass of the motors and gears are estimated based on the required torque and speed, obtained from the trajectory and force-distribution. This study shows that a well-designed multi-arm system can in fact have a lower mass than a single-arm system. Further optimization demonstrates that a multi-arm system, when designed as a complete system rather than individual parts, can significantly reduce the total system mass

Development of an assisted-teleoperation system for a dual-manipulator nuclear decommissioning robot

This thesis concerns a robotic platform that is being used for research into assisted tele–operation for common nuclear decommissioning tasks, such as remote handling and pipe cutting. The machine consists of dual, seven–function, hydraulically actuated HYDROLEK manipulators mounted (in prior research) on a mobile BROKK base unit. Whilst the original system was operated by remote control, the present thesis focusses on the development of a visual servoing system, in which the user selects the object of interest from an on–screen image, whilst the computer control system determines and implements via feedback control the required position and orientation of the manipulators. Novel research contributions are made in three main areas: (i) the development of a detailed mechanistic model of the system; (ii) the development and preliminary testing in the laboratory of the new assisted–teleoperation user interface; and (iii) the development of improved control systems for joint angle set point tracking, and their systematic, quantitative comparison via simulation and experiment. The mechanistic model builds on previous work, while the main novelty in this thesis relates to the hydraulic component of the model, and the development and evaluation of a multi–objective genetic algorithm framework to identify the unknown parameter values. To improve on the joystick direct teleoperation currently used as standard in the nuclear industry, which is slow and requires extensive operator training, the proposed assisted–teleoperation makes use of a camera mounted on the robot. Focussing on pipe cutting as an example, the new system ensures that one manipulator automatically grasps the user–selected pipe, and appropriately positions the second for a cutting operation. Initial laboratory testing (using a plastic pipe) shows the efficacy of the approach for positioning the manipulators, and suggests that for both experienced and inexperienced users, the task is completed significantly faster than via tele-operation. Finally, classical industrial, fuzzy logic, and novel state dependent parameter approaches to control are developed and compared, with the aim being to determine a relatively simple controller that yields good performance for the hydraulic manipulators. An improved, more structured method of dealing with the dead–zone characteristics is developed and implemented, replacing the rather ad hoc approach that had been utilised in previous research for the same machine

Path Planning For Persistent Surveillance Applications Using Fixed-Wing Unmanned Aerial Vehicles

This thesis addresses coordinated path planning for fixed-wing Unmanned Aerial Vehicles (UAVs) engaged in persistent surveillance missions. While uniquely suited to this mission, fixed wing vehicles have maneuver constraints that can limit their performance in this role. Current technology vehicles are capable of long duration flight with a minimal acoustic footprint while carrying an array of cameras and sensors. Both military tactical and civilian safety applications can benefit from this technology. We make three main contributions: C1 A sequential path planner that generates a C2 flight plan to persistently acquire a covering set of data over a user designated area of interest. The planner features the following innovations: • A path length abstraction that embeds kino-dynamic motion constraints to estimate feasible path length • A Traveling Salesman-type planner to generate a covering set route based on the path length abstraction • A smooth path generator that provides C2 routes that satisfy user specified curvature constraints C2 A set of algorithms to coordinate multiple UAVs, including mission commencement from arbitrary locations to the start of a coordinated mission and de-confliction of paths to avoid collisions with other vehicles and fixed obstacles iv C3 A numerically robust toolbox of spline-based algorithms tailored for vehicle routing validated through flight test experiments on multiple platforms. A variety of tests and platforms are discussed. The algorithms presented are based on a technical approach with approximately equal emphasis on analysis, computation, dynamic simulation, and flight test experimentation. Our planner (C1) directly takes into account vehicle maneuverability and agility constraints that could otherwise render simple solutions infeasible. This is especially important when surveillance objectives elevate the importance of optimized paths. Researchers have devel oped a diverse range of solutions for persistent surveillance applications but few directly address dynamic maneuver constraints. The key feature of C1 is a two stage sequential solution that discretizes the problem so that graph search techniques can be combined with parametric polynomial curve generation. A method to abstract the kino-dynamics of the aerial platforms is then presented so that a graph search solution can be adapted for this application. An A* Traveling Salesman Problem (TSP) algorithm is developed to search the discretized space using the abstract distance metric to acquire more data or avoid obstacles. Results of the graph search are then transcribed into smooth paths based on vehicle maneuver constraints. A complete solution for a single vehicle periodic tour of the area is developed using the results of the graph search algorithm. To execute the mission, we present a simultaneous arrival algorithm (C2) to coordinate execution by multiple vehicles to satisfy data refresh requirements and to ensure there are no collisions at any of the path intersections. We present a toolbox of spline-based algorithms (C3) to streamline the development of C2 continuous paths with numerical stability. These tools are applied to an aerial persistent surveillance application to illustrate their utility. Comparisons with other parametric poly nomial approaches are highlighted to underscore the benefits of the B-spline framework. Performance limits with respect to feasibility constraints are documented

Autonomous Rendezvous with a non-cooperative satellite: trajectory planning and control

Con la nascita di nuove problematiche e nuove esigenze in ambito spaziale, le più importanti riguardanti il tema della mitigazione dei detriti spaziali o dell’assistenza e del servizio dei satelliti in orbita, lo scenario di rendez-vous autonomo tra un satellite inseguitore e un satellite target non cooperativo sta diventando sempre più centrale, ambizioso e accattivante. Il grande scoglio da superare, tuttavia, consiste nell’individuazione di una strategia di approccio robusta e vincente: mentre l’esecuzione di una manovra di rendez-vous e docking o cattura con satellite cooperativo è già stata collaudata e possiede una consolidata eredità di volo, il rendez-vous autonomo con satellite non cooperativo ed in stato di tombolamento è uno scenario agli albori, con pochi studi al riguardo. Lo scopo di questa tesi consiste nell’identificazione di una strategia di approccio che consideri le principali problematiche legate al tema in questione, ovvero la non-cooperazione e le scarse informazioni sullo stato di moto del target da raggiungere. Queste due complicazioni portano alla necessità di eseguire un moto di ispezione del satellite target e alla considerazione di numerosi vincoli nella progettazione della traiettoria di ispezione e di approccio. Un controllore adatto a trattare questo problema complesso e multi-vincolato è il Model Predictive Controller, in forma lineare o non lineare, abbinato ad un filtro di Kalman. La capacità di questo controllore di previsione e pianificazione di una traiettoria d’approccio, a partire da stime di posizione relativa tra target e inseguitore, permette di portare a termine la manovra in modo sicuro e robusto.According to the rise of new problems and new demands in the space field, the most important concerning the mitigation of space debris or the spacecraft on-orbit servicing and assistance themes, the Autonomous Rendezvous scenario between a chase satellite and a non-cooperative target satellite is becoming increasingly significant, ambitious, and attractive. The main issue to overcome, however, consists in the identification of a robust and successful approach strategy: while the execution of a rendezvous and docking or capture maneuver with a cooperative satellite has already been tested and holds a solid flight heritage, the autonomous rendezvous with a non-cooperative satellite in a state of tumbling motion is a scenario in the early days, with few studies about it and a not yet mature technology. The aim of this thesis consists in the identification of an approach strategy that deals with the main challenges related to the considered problem, namely non-cooperativeness and exiguous information about the target to be reached. These two issues lead to the need of performing an inspection motion and considering several constraints in the trajectory design. A controller suitable to handle this complex and multi-constrained problem is the Model Predictive Controller, in a linear or non-linear form, paired with a Kalman filter. The ability of this controller to predict and plan an approaching trajectory, starting from estimates of the relative position between the target and the chaser, allows to complete the approaching maneuver safely and in a robust way

Perception architecture exploration for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.In emerging autonomous and semi-autonomous vehicles, accurate environmental perception by automotive cyber physical platforms are critical for achieving safety and driving performance goals. An efficient perception solution capable of high fidelity environment modeling can improve Advanced Driver Assistance System (ADAS) performance and reduce the number of lives lost to traffic accidents as a result of human driving errors. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. For instance, there is an inherent accuracy versus latency trade-off between one stage and two stage object detectors which makes selecting an enhanced object detector from a diverse range of choices difficult. Further, even if a perception architecture was equipped with an ideal object detector performing high accuracy and low latency inference, the relative position and orientation of selected sensors (e.g., cameras, radars, lidars) determine whether static or dynamic targets are inside the field of view of each sensor or in the combined field of view of the sensor configuration. If the combined field of view is too small or contains redundant overlap between individual sensors, important events and obstacles can go undetected. Conversely, if the combined field of view is too large, the number of false positive detections will be high in real time and appropriate sensor fusion algorithms are required for filtering. Sensor fusion algorithms also enable tracking of non-ego vehicles in situations where traffic is highly dynamic or there are many obstacles on the road. Position and velocity estimation using sensor fusion algorithms have a lower margin for error when trajectories of other vehicles in traffic are in the vicinity of the ego vehicle, as incorrect measurement can cause accidents. Due to the various complex inter-dependencies between design decisions, constraints and optimization goals a framework capable of synthesizing perception solutions for automotive cyber physical platforms is not trivial. We present a novel perception architecture exploration framework for automotive cyber- physical platforms capable of global co-optimization of deep learning and sensing infrastructure. The framework is capable of exploring the synthesis of heterogeneous sensor configurations towards achieving vehicle autonomy goals. As our first contribution, we propose a novel optimization framework called VESPA that explores the design space of sensor placement locations and orientations to find the optimal sensor configuration for a vehicle. We demonstrate how our framework can obtain optimal sensor configurations for heterogeneous sensors deployed across two contemporary real vehicles. We then utilize VESPA to create a comprehensive perception architecture synthesis framework called PASTA. This framework enables robust perception for vehicles with ADAS requiring solutions to multiple complex problems related not only to the selection and placement of sensors but also object detection, and sensor fusion as well. Experimental results with the Audi-TT and BMW Minicooper vehicles show how PASTA can intelligently traverse the perception design space to find robust, vehicle-specific solutions

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

The urban real-time traffic control (URTC) system : a study of designing the controller and its simulation

The growth of the number of automobiles on the roads in China has put higher demands on the traffic control system that needs to efficiently reduce the level of congestion occurrence, which increases travel delay, fuel consumption, and air pollution. The traffic control system, urban real-time traffic control system based on multi-agent (MA-URTC) is presented in this thesis. According to the present situation and the traffic's future development in China, the researches on intelligent traffic control strategy and simulation based on agent lays a foundation for the realization of the system. The thesis is organized as follows: The first part focuses on the intersection' real-time signal control strategy. It contains the limitations of current traffic control systems, application of artificial intelligence in the research, how to bring the dynamic traffic flow forecast into effect by combining the neural network with the genetic arithmetic, and traffic signal real-time control strategy based on fuzzy control. The author uses sorne simple simulation results to testify its superiority. We adopt the latest agent technology in designing the logical structure of the MA-URTC system. By exchanging traffic flows information among the relative agents, MA-URTC provides a new concept in urban traffic control. With a global coordination and cooperation on autonomy-based view of the traffic in cities, MA-URTC anticipates the congestion and control traffic flows. It is designed to support the real-time dynamic selection of intelligent traffic control strategy and the real-time communication requirements, together with a sufficient level of fault-tolerance. Due to the complexity and levity of urban traffic, none strategy can be universally applicable. The agent can independently choose the best scheme according to the real-time situation. To develop an advanced traffic simulation system it can be helpful for us to find the best scheme and the best switch-point of different schemes. Thus we can better deal with the different real-time traffic situations. The second part discusses the architecture and function of the intelligent traffic control simulation based on agent. Meanwhile the author discusses the design model of the vehicle-agent, road agent in traffic network and the intersection-agent so that we can better simulate the real-time environment. The vehicle-agent carries out the intelligent simulation based on the characteristics of the drivers in the actual traffic condition to avoid the disadvantage of the traditional traffic simulation system, simple-functioned algorithm of the vehicles model and unfeasible forecasting hypothesis. It improves the practicability of the whole simulation system greatly. The road agent's significance lies in its guidance of the traffic participants. It avoids the urban traffic control that depends on only the traffic signal control at intersection. It gives the traffic participants the most comfortable and direct guidance in traveling. It can also make a real-time and dynamic adjustment on the urban traffic flow, thus greatly lighten the pressure of signal control in intersection area. To sorne extent, the road agent is equal to the pre-caution mechanism. In the future, the construction of urban roads tends to be more intelligent. Therefore, the research on road agent is very important. All kinds of agents in MA-URTC are interconnected through a computer network. In the end, the author discusses the direction of future research. As the whole system is a multi-agent system, the intersection, the road and the vehicle belongs to multi-agent system respectively. So the emphasis should be put on the structure design and communication of all kinds of traffic agents in the system. Meanwhile, as an open and flexible real-time traffic control system, it is also concerned with how to collaborate with other related systems effectively, how to conform the resources and how to make the traffic participants anywhere throughout the city be in the best traffic guidance at all times and places. To actualize the genuine ITS will be our final goal. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Artificial Intelligence, Computer simulation, Fuzzy control, Genetic Algorithm, Intelligent traffic control, ITS, Multi-agent, Neural Network, Real-time

High-precision grasping and placing for mobile robots

This work presents a manipulation system for multiple labware in life science laboratories using the H20 mobile robots. The H20 robot is equipped with the Kinect V2 sensor to identify and estimate the position of the required labware on the workbench. The local features recognition based on SURF algorithm is used. The recognition process is performed for the labware to be grasped and for the workbench holder. Different grippers and labware containers are designed to manipulate different weights of labware and to realize a safe transportation

XVI Congresso da Associação Portuguesa de Investigação Operacional: livro de resumos

Fundação para a Ciência e a Tecnologia - FC

Human-Robot Collaborations in Industrial Automation

Technology is changing the manufacturing world. For example, sensors are being used to track inventories from the manufacturing floor up to a retail shelf or a customer’s door. These types of interconnected systems have been called the fourth industrial revolution, also known as Industry 4.0, and are projected to lower manufacturing costs. As industry moves toward these integrated technologies and lower costs, engineers will need to connect these systems via the Internet of Things (IoT). These engineers will also need to design how these connected systems interact with humans. The focus of this Special Issue is the smart sensors used in these human–robot collaborations