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The use of robotics in hazardous environments is becoming more common, where autonomy can handle the dull, dirty and dangerous jobs that humans have previously supported. This thesis focuses on the design of a helicopter deployable unmanned ground vehicle for use in hazardous environments, and presents the benefits of heterogeneous unmanned vehicle teams for operation in beyond line-of-site hazardous environments. The design of a ground robot that is capable of being flown on the undercarriage of a Yamaha RMAX unmanned air vehicle is presented. The robot is size, weight, and power limited and must be capable of traversing rough, unstructured terrain. The results of testing show that the design criteria for size, weight, and mobility are met. A path planning algorithm is developed using the A* search algorithm for the planning of optimal paths through rough terrain. The algorithm makes use of a vehicle/terrain interaction model to compute the cost of path traversal. In the CONOPS, the terrain model is generated real-time during a mission through the use of a stereovision system carried on the helicopter, which station-keeps above the ground robot. The algorithm simulates the robot on the terrain and presents the best feasible path to the operator to aid in teleoperated robot navigation. Simulations of the planning algorithm provided feasible paths over a rough terrain environment. A user study was conducted that evaluates the abilities of both mono- and stereo-vision systems in providing the teleoperator with adequate situational awareness with the intent of proving that stereovision data is more effective at aiding the user in making timely navigation decisions. The results of the study showed that the helicopter-mounted stereovision system was more efficient than the monovision system with respect to navigation time, the number of invalid moves, and total moves required for navigation of a simulated rough terrain environment. / Master of Science

Affordable and readily available automation options for plant research remain scarce, however with the availability of such a system, many research tasks can be streamlined. In this project, we demonstrate a prototype of such an open-source, low-cost, heterogeneous robotic system called Mini T-Rex. We combine two over-the-counter robots and leverage the ROS2 framework to control this heterogeneous system. This system provides a unique advantage of sensor-to-plant method to capture multi-view images at any angle and distance within the workspace. We demonstrate how making a digital twin in ROS2 can help to control a heterogeneous system via abstracted hardware control. We also talk about I2GROW Oasis which is a robotic system consisting of a remotely controlled robot with the ability to capture top-view images. In this thesis we describe the hardware and software design of both these robotic systems. To use this robotic system, the plants can be grown on a growth bed or a hydroponic system below the Mini T-Rex robot, and the camera will approach the plant without any contact with the plants due to the precise control of the robotic manipulator. We used the system to capture several large data sets of 3D phenotypic data for Solanum lycopersicum, Lactuca sativa, and Thlaspi. In conclusion, we have developed a 9-degree of freedom, fully open-source heterogeneous robotic system capable of multi-view, camera-to plant image capture for plant 3D model reconstruction called Mini T-Rex. We show how to use gantry like robots for phenotyping and create longitudinal datasets by automating these high precision robotic systems. / Master of Science / Robotics are being widely used for automating tasks that are monotonous and require high precision. However, developing such application specific robots in itself is a complicated and tedious task. Having different aspects like mechanical design, robot fabrication, software design makes it difficult for any individual or small groups to develop such robots. In order to facilitate plant researchers who may not have any experience in designing robots, we have developed a general robotic system that can be easily assembled and adapted for applications. In this thesis, we discuss how this robotic system can be made using over the counter robots and discuss how the software makes it intelligent enough such that it can navigate the course without any collisions and control the robots as if they are part of one system rather than two different robots that are controlled individually. This enables using the vendor provided software rather than designing the entire robot from scratch. We also show another robot kits, the FarmBot, which can be assembled and adapted to particular use case of monitoring hydroponically growing crops. We demonstrate how this robot can be used as part of complex systems and how it can be automated to collect images to monitor plant growth. We describe in detail of how a user can go from computer aided design (CAD) to hardware control of the robot, and how this system can be used for phenotyping of plants namely early girl tomato, lettuce, and pennycress.

Connected and Automated Vehicles (CAVs) represent a growing area of study in robotics and automotive research. Their potential benefits of increased traffic flow, reduced on-road accident, and improved fuel economy make them an attractive option. While some autonomous features such as Adaptive Cruise Control and Lane Keep Assist are already integrated into consumer vehicles, they are limited in scope and require innovation to realize fully autonomous vehicles. This work addresses the design problems of perception and planning in CAVs. A decentralized sensor fusion system is designed using Multi-target tracking to identify targets within a vehicle's field of view, enumerate each target with the lane it occupies, and highlight the most important object (MIO) for Adaptive cruise control. Its performance is tested using the Optimal Sub-pattern Assignment (OSPA) metric and correct assignment rate of the MIO. The system has an average accuracy assigning the MIO of 98%. The rest of this work considers the coordination of multiple CAVs from a multi-agent motion planning perspective. A centralized planning algorithm is applied to a space similar to a traffic intersection and is demonstrated empirically to be twice as fast as existing multi-agent planners., making it suitable for real-time planning environments. / Master of Science / Connected and Automated Vehicles are an emerging area of research that involve integrating computational components to enable autonomous driving. This work considers two of the major challenges in this area of research. The first half of this thesis considers how to design a perception system in the vehicle that can correctly track other vehicles and assess their relative importance in the environment. A sensor fusion system is designed which incorporates information from different sensor types to form a list of relevant target objects. The rest of this work considers the high-level problem of coordination between autonomous vehicles. A planning algorithm which plans the paths of multiple autonomous vehicles that is guaranteed to prevent collisions and is empirically faster than existing planning methods is demonstrated.

abstract: In this thesis, a new approach to learning-based planning is presented where critical regions of an environment with low probability measure are learned from a given set of motion plans. Critical regions are learned using convolutional neural networks (CNN) to improve sampling processes for motion planning (MP). In addition to an identification network, a new sampling-based motion planner, Learn and Link, is introduced. This planner leverages critical regions to overcome the limitations of uniform sampling while still maintaining guarantees of correctness inherent to sampling-based algorithms. Learn and Link is evaluated against planners from the Open Motion Planning Library (OMPL) on an extensive suite of challenging navigation planning problems. This work shows that critical areas of an environment are learnable, and can be used by Learn and Link to solve MP problems with far less planning time than existing sampling-based planners. / Dissertation/Thesis / Masters Thesis Computer Science 2019

<p dir="ltr">Path-tracking control is an integral part of motion planning in autonomous vehicles, in which the vehicle's lateral and longitudinal positions are controlled by a control system that will provide acceleration and steering angle commands to ensure accurate tracking of longitudinal and lateral movements in reference to a pre-defined trajectory. Extensive research has been conducted to address the growing need for efficient algorithms in this area. In this dissertation, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a Deep Reinforcement Learning model is developed to facilitate the control of longitudinal speed. A Deep Deterministic Policy Gradient algorithm is employed as the primary algorithm in training the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed using Neural Networks to control the steering angle of the vehicle with the main goal of following a reference trajectory. Then, a path-planning algorithm is developed using a hybrid A* planner. Finally, the longitudinal and lateral control models are coupled together to obtain a complete path-tracking controller that follows a path generated by the hybrid A* algorithm at a wide range of vehicle speeds. The state-of-the-art path-tracking controller is also built using Model Predictive Control and Stanley control to evaluate the performance of the proposed model. The results showed the effectiveness of both proposed models in the same scenario, in terms of velocity error, lateral yaw angle error, and lateral distance error. The results from the simulation show that the developed hybrid A* algorithm has good performance in comparison to the state-of-the-art path planning algorithms.</p>

本研究在虛擬環境中發展出一套符合攝影學法則的即時智慧型攝影機模組，當使用者操縱化身時能夠給予攝影機自動規劃之能力，不需要花費額外的心力控制攝影機。智慧型攝影機的能力包含了規劃不被障礙物遮蔽的跟蹤拍攝，以及當攝影機進入險惡的環境時，自動在兩台攝影機之間進行符合攝影學法則的轉切。此外，此智慧型攝影機模組能夠依照不同使用者的偏好，產生不同結果的攝影機規劃。我們實作了此一智慧型攝影機的系統，並以實例說明此系統的有效性。 / We have developed a real-time intelligent camera module with a set of cinematography in a virtual environment. Its automatic camera planning capacity allows that the users to spend less effort in controlling the camera when manipulating an avatar. An intelligent camera should be able to plan unobstractive tracking motion and necessary cuts automatically between two cameras with the rule of cinematography. In addition, the results of the camera planning can be customized by setting appropriate parameters in accordance with the user’s preferences. We have implemented this intelligent camera system, and demonstrated the effectiveness of this system through several examples

The subject of this thesis is the motion planning algorithm known as the continuation method. To solve motion planning problems, the continuation method proceeds by lifting curves in state space to curves in control space; the lifted curves are the solutions of special initial value problems called path-lifting equations. To validate this procedure, three distinct obstructions must be overcome. The first obstruction is that the endpoint maps of the control system under study must be twice continuously differentiable. By extending a result of A. Margheri, we show that this differentiability property is satisfied by an inclusive class of time-varying fully nonlinear control systems. The second obstruction is the existence of singular controls, which are simply the singular points of a fixed endpoint map. Rather than attempting to completely characterize such controls, we demonstrate how to isolate control systems for which no controls are singular. To this end, we build on the work of S. A. Vakhrameev to obtain a necessary and sufficient condition. In particular, this result accommodates time-varying fully nonlinear control systems. The final obstruction is that the solutions of path-lifting equations may not exist globally. To study this problem, we work under the standing assumption that the control system under study is control-affine. By extending a result of Y. Chitour, we show that the question of global existence can be resolved by examining Lie bracket configurations and momentum functions. Finally, we show that if the control system under study is completely unobstructed with respect to a fixed motion planning problem, then its corresponding endpoint map is a fiber bundle. In this sense, we obtain a necessary condition for unobstructed motion planning by the continuation method. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2012-12-18 20:53:43.272

In recent years, autonomous driving has become an increasingly practical technology. With state-of-the-art computer and sensor engineering, autonomous vehicles may be produced and widely used for travel and logistics in the near future. They have great potential to reduce traffic accidents, improve transportation efficiency, and release people from driving tasks while commuting. Researchers have built autonomous vehicles that can drive on public roads and handle normal surrounding traffic and obstacles. However, in situations like lane changing and merging, the autonomous vehicle faces the challenge of performing smooth interaction with human-driven vehicles. To do this, autonomous vehicle intelligence still needs to be improved so that it can better understand and react to other human drivers on the road. In this thesis, we argue for the importance of implementing ”socially cooperative driving”, which is an integral part of everyday human driving, in autonomous vehicles. An intention-integrated Prediction- and Cost function-Based algorithm (iPCB) framework is proposed to enable an autonomous vehicles to perform cooperative social behaviors. We also propose a behavioral planning framework to enable the socially cooperative behaviors with the iPCB algorithm. The new architecture is implemented in an autonomous vehicle and can coordinate the existing Adaptive Cruise Control (ACC) and Lane Centering interface to perform socially cooperative behaviors. The algorithm has been tested in over 500 entrance ramp and lane change scenarios on public roads in multiple cities in the US and over 10; 000 in simulated case and statistical testing. Results show that the proposed algorithm and framework for autonomous vehicle improves the performance of autonomous lane change and entrance ramp handling. Compared with rule-based algorithms that were previously developed on an autonomous vehicle for these scenarios, over 95% of potentially unsafe situations are avoided.