As the number of spacecraft in simultaneous operation continues to grow, there is an increased dependency on ground-based navigation support. The current baseline system for deep space navigation utilizes Earth-based radiometric tracking, which requires long duration, often global, observations to perform orbit determination and generate a state update. The age, complexity, and high utilization of the assets that make up the Deep Space Network (DSN) pose a risk to spacecraft navigation performance. With increasingly complex mission operations, such as automated asteroid rendezvous or pinpoint planetary landing, the need for high accuracy and autonomous navigation capability is further reinforced. The Network-Based Navigation (NNAV) method developed in this research takes advantage of the growing inter-spacecraft communication network infrastructure to allow for autonomous state measurement. By embedding navigation headers into the data packets transmitted between nodes in the communication network, it is possible to provide an additional source of navigation capability. Simulation results indicate that as NNAV is implemented across the deep space network, the state estimation capability continues to improve, providing an embedded navigation network. To analyze the capabilities of NNAV, an analysis and simulation framework is designed that integrates navigation and communication analysis. Model-Based Systems Engineering (MBSE) and Agent-Based Modeling (ABM) techniques are utilized to foster a modular, expandable, and robust framework. This research has developed the Space Navigation Analysis and Performance Evaluation (SNAPE) framework. This framework allows for design, analysis, and optimization of deep space navigation and communication architectures. SNAPE captures high-level performance requirements and bridges them to specific functional requirements of the analytical implementation. The SNAPE framework is implemented in a representative prototype environment using the Python language and verified using industry standard packages. The capability of SNAPE is validated through a series of example test cases. These analyses focus on the performance of specific state measurements to state estimation performance, and demonstrate the core analytic functionality of the framework. Specific cases analyze the effects of initial error and measurement uncertainty on state estimation performance. The timing and frequency of state measurements are also investigated to show the need for frequent state measurements to minimize navigation errors. The dependence of navigation accuracy on timing stability and accuracy is also demonstrated. These test cases capture the functionality of the tool as well as validate its performance. The SNAPE framework is utilized to capture and analyze NNAV, both conceptually and analytically. Multiple evaluation cases are presented that focus on the Mars Science Laboratory's (MSL) Martian transfer mission phase. These evaluation cases validate NNAV and provide concrete evidence of its operational capability for this particular application. Improvement to onboard state estimation performance and reduced reliance on Earth-based assets is demonstrated through simulation of the MSL spacecraft utilizing NNAV processes and embedded packets within a limited network containing DSN and MRO. From the demonstrated state estimation performance, NNAV is shown to be a capable and viable method of deep space navigation. Through its implementation as a state augmentation method, the concept integrates with traditional measurements and reduces the dependence on Earth-based updates. Future development of this concept focuses on a growing network of assets and spacecraft, which allows for improved operational flexibility and accuracy in spacecraft state estimation capability and a growing solar system-wide navigation network.

This thesis focuses on the theory and implementation of visual navigation techniques for Autonomous Air Vehicles in outdoor environments. The target of this study is to fuse and cooperatively develop an incremental map for multiple air vehicles under the application of Simultaneous Location and Mapping (SLAM). Without loss of generality, two unmanned air vehicles (UAVs) are investigated for the generation of ground maps from current and a priori data. Each individual UAV is equipped with inertial navigation systems and external sensitive elements which can provide the possible mixture of visible, thermal infrared (IR) image sensors, with a special emphasis on the stereo digital cameras. The corresponding stereopsis is able to provide the crucial three-dimensional (3-D) measurements. Therefore, the visual aerial navigation problems tacked here are interpreted as stereo vision based SLAM (vSLAM) for both single and multiple UAVs applications. To begin with, the investigation is devoted to the methodologies of feature extraction. Potential landmarks are selected from airborne camera images as distinctive points identified in the images are the prerequisite for the rest. Feasible feature extraction algorithms have large influence over feature matching/association in 3-D mapping. To this end, effective variants of scale-invariant feature transform (SIFT) algorithms are employed to conduct comprehensive experiments on feature extraction for both visible and infrared aerial images. As the UAV is quite often in an uncertain location within complex and cluttered environments, dense and blurred images are practically inevitable. Thus, it becomes a challenge to find feature correspondences, which involves feature matching between 1st and 2nd image in the same frame, and data association of mapped landmarks and camera measurements. A number of tests with different techniques are conducted by incorporating the idea of graph theory and graph matching. The novel approaches, which could be tagged as classification and hypergraph transformation (HGTM) based respectively, have been proposed to solve the data association in stereo vision based navigation. These strategies are then utilised and investigated for UAV application within SLAM so as to achieve robust matching/association in highly cluttered environments. The unknown nonlinearities in the system model, including noise would introduce undesirable INS drift and errors. Therefore, appropriate appraisals on the pros and cons of various potential data filtering algorithms to resolve this issue are undertaken in order to meet the specific requirements of the applications. These filters within visual SLAM were put under investigation for data filtering and fusion of both single and cooperative navigation. Hence updated information required for construction and maintenance of a globally consistent map can be provided by using a suitable algorithm with the compromise between computational accuracy and intensity imposed by the increasing map size. The research provides an overview of the feasible filters, such as extended Kalman Filter, extended Information Filter, unscented Kalman Filter and unscented H Infinity Filter. As visual intuition always plays an important role for humans to recognise objects, research on 3-D mapping in textures is conducted in order to fulfil the purpose of both statistical and visual analysis for aerial navigation. Various techniques are proposed to smooth texture and minimise mosaicing errors during the reconstruction of 3-D textured maps with vSLAM for UAVs. Finally, with covariance intersection (CI) techniques adopted on multiple sensors, various cooperative and data fusion strategies are introduced for the distributed and decentralised UAVs for Cooperative vSLAM (C-vSLAM). Together with the complex structure of high nonlinear system models that reside in cooperative platforms, the robustness and accuracy of the estimations in collaborative mapping and location are achieved through HGTM association and communication strategies. Data fusion among UAVs and estimation for visual navigation via SLAM were impressively verified and validated in conditions of both simulation and real data sets. / © Cranfield University, 2013

The aim of this thesis is to investigate how well a generic monocular camera, placed on the vehicle, can be employed to localize an autonomous vehicle in a warehouse setting. The main function is to ascertain which zone the vehicle is currently in, as well as update the status when entering a new zone. Two zones are defined, where one has a lower allowed top velocity and the other a higher one. For this purpose ArUco markers are used to signal the system as to where it currently is. Markers are strategically placed around the laboratory area to saturate the environment with possible detections. Multiple sequences are recorded while varying camera placement, angles, and paths to determine the optimal number and placement of markers. In addition to this, a SLAM solution is tested in order to explore what benefits can be found. The idea is to provide fine-grained localization as well as a map of the warehouse environment, to provide more options for further development. To solve the SLAM problem, an implemented particle filter approach initializes a set of particles uniformly distributed within the world frame. For each frame, the particles undergo pose prediction, weight assignment based on likelihood, and resampling. This iterative process gradually converges the particles toward the camera's true position. Visual odometry techniques are used to estimate the camera's ego-motion. The process involves acquiring a sequence of images, detecting distinctive features, matching features between consecutive frames, estimating camera motion, and optionally applying local optimization techniques for further refinement. The implementation shows promise and all test cases performed during the project have been successful as for the zone localization. The SLAM solution can detect and track specific features or landmarks over consecutive frames. By triangulating the positions of these features, their depth and distance can be determined. However, the visualization of these features on a top-down map, which was part of the plan, has not been completed yet despite finishing the particle filter implementation.

Micro Aerial Vehicles (MAVs) provide a highly capable, agile platform, ideally suited for intelligence/surveillance/reconnaissance missions, urban search and rescue, and scientific exploration. Critical to the success of these tasks is a system which moves au-tonomously through an unknown, obstacle-strewn, GPS-denied environment. Classical simultaneous localization and mapping (SLAM) approaches rely on large, heavy sensors to generate 3-D information about a MAV’s surroundings, severely limiting its abilities. This motivates a study of Parallel Tracking and Mapping (PTAM), an algorithm requiring only a single camera to provide 3-D data to an autonomous navigation system. Metric properties of 3-D MAV pose estimates are compared with physical measurements to ex-plore tracking accuracy. Additionally, a discrete wavelet transform-based keypoint detec-tor is implemented for a feasibility study on improving map density in low-visual-detail environments. Finally, a system is presented that integrates PTAM, autonomous MAV control, and a human interface for manual control and data logging.

To map the subsurface Ground Penetrating Radar (GPR) can be used in a non-invasive way. It is currently done manually by pushing a wheeled device on a handlebar. This thesis suggests an alternative method using an integrated autonomous solution. To ac- complice that: several sensors were fused to give the robot perception of the world, the ability to localize itself within it, and plan a path to reach the goal. Detecting algorithms were implemented and tested to ensure the robot could handle a dynamic and compli- cated world. The results showed that the robot could independently navigate in a grid pattern conducting GPR surveys while avoiding obstacles and finding a safe route. All this will allow for collecting GPR data with precise localization measurements and in paths more detailed than a human operator could. In addition, it enables the operator to be at a safe distance in dangerous environments and to search large areas.

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This thesis describes an ongoing project of which the purpose is designing and developing techniques and algorithms for autonomous off-road vehicles. The focus is on some of the components necessary to accomplish autonomous navigation, which involves sensing and moving safely along a user-defined path in a dynamic forest environment. The work is part of a long-term vision in the forest industry of developing an unmanned shuttle that transports timber from the felling area to the main roads for further transportation. A new path-tracking algorithm is introduced and demonstrated as superior to standard algorithms, such as Follow the Carrot and Pure Pursuit. This is accomplished by using recorded data from a path-learning phase. By using the recorded steering angle, the curvature of the path is automatically included in the final steering command. Localization is accomplished by a neural network that fuses data from a Real-Time Kinematic Differential GPS/GLONASS, a gyro, and wheel odometry. Test results are presented for path tracking and localization accuracy from runs conducted on a full-sized forest machine. A large part of the work has been design and implementation of a general software platform for research in autonomous vehicles. The developed algorithms and software have been implemented and tested on a full-size forest machine supplied by our industrial partner Komatsu Forest AB. Results from successful field tests with autonomous path tracking, including obstacle avoidance, are presented.

The clothoid, also known as the Cornu spiral, is a curve generated by linearly increasing or decreasing curvature as a function of arc length. The clothoid has been widely accepted as a logical curve for transitioning from straight segments to circle arcs in roads and railways, because a vehicle following the curve at constant speed will have a constant change of centripetal acceleration. Clothoids have also been widely adopted in planning potential paths for autonomous vehicle navigation. They have been viewed as useful representations of possible trajectories that are dynamically feasible. Surprisingly, the assumptions that underlie this choice appear to be lightly treated or ignored in past literature. This thesis will examine three key assumptions that are implicitly made when assuming that a vehicle will follow a clothoid path. The first assumption is that the vehicle's steering mechanism will produce a linear change in turning radius for a constant rate input. This assumption is loosely referred to as the "bicycle model" and it relates directly to the kinematic parameters of the steering mechanism. The second assumption is that the steering actuator can provide a constant steering velocity. In other words, the actuator controlling the steering motion can instantaneously change from one rate to another. The third assumption is that the vehicle is traveling at a constant velocity. By definition, the clothoid is a perfect representation of a vehicle traveling at constant velocity with a constant rate of change in steering curvature. The goal of this research was to examine the accuracy of these assumptions for a typical Ackermann-steered ground vehicle. Both theoretical and experimental results are presented. The vehicle that was used as an example in this study was a modified Club Car Pioneer XRT 1500. This Ackermann-steered vehicle was modified for autonomous navigation and was one of Virginia Tech's entries in the DARPA 2005 Grand Challenge. As in typical operation, path planning was conducted using the classic clothoid curve model. The vehicle was then commanded to drive a selected path, but with variations in speed and steering rate that are inherent to the real system. The validity of the three assumptions discussed above were examined by comparing the actual vehicle response to the planned clothoid. This study determined that the actual paths driven by the vehicle were generally a close match to the originally planned theoretical clothoid path. In this study, the actual kinematics of the Ackermann vehicle steering system had only a small effect on the driven path. This indicates that the bicycle model is a reasonable simplification, at least for the case studied. The assumption of constant velocity actuation of the steering system also proved to be reasonably accurate. The greatest deviation from the planned clothoid path resulted from the nonlinear velocity of the vehicle along the path, especially when accelerating from a stop. Nevertheless, the clothoid path plan generally seems to be a good representation of actual vehicle motion, especially when the planned path is updated frequently. / Master of Science