Autonomous Navigation for Mobile Robots: Machine Learning-based Techniques for Obstacle Avoidance

Department of System Design and Control EngineeringAutonomous navigation of unmanned aerial vehicles (UAVs) has posed several challenges due to the limitations regarding the number and size of sensors that can be attached to the mobile robots. Although sensors such as LIDARs that directly obtain distance information of the surrounding environment have proven to be effective for obstacle avoidance, the weight and cost of the sensor contribute to the restrictions on usage for UAVs as recent trends require smaller sizes of UAVs. One practical option is the utilization of monocular vision sensors which tend to be lightweight and have a relatively low cost, yet still the main drawback is that it is difficult to draw a certain rule from the sensor data. Conventional methods regarding visual navigation makes use of features within the image data or estimate the depth of the image using various techniques such as optical flow. These features and methodologies however still rely on human-based rules and features, meaning that robustness can become an issue. A more recent approach to vision-based obstacle avoidance exploits heuristic methods based on artificial intelligence such as deep learning technologies, which have shown state-of-the-art performance in fields such as image processing or voice recognition. These technologies are capable of automatically selecting important features for classification or prediction tasks, hence allowing superior performance. Such heuristic methods have proven to be more efficient as the rules and features that are drawn from the image are automatically determined, unlike conventional methods where the rules and features are explicitly determined by humans. In this thesis, we propose an imitation learning framework based on deep learning technologies that can be applied to the obstacle avoidance of UAVs, where the neural networks in this framework are trained upon the flight data obtained from human experts, extracting the necessary features and rules to carry out designated tasks. The system introduced in this thesis mainly consists of three parts: the data acquisition and preprocessing phase, the model training phase, and the model application phase. A CNN (Convolutional Neural Network), 3D-CNN, and a DNN (Deep Neural Network) will each be applied to the framework and tested with respect to the collision ratios to validate the obstacle avoidance performance.ope

Visual-Inertial Sensor Fusion Models and Algorithms for Context-Aware Indoor Navigation

Positioning in navigation systems is predominantly performed by Global Navigation Satellite Systems (GNSSs). However, while GNSS-enabled devices have become commonplace for outdoor navigation, their use for indoor navigation is hindered due to GNSS signal degradation or blockage. For this, development of alternative positioning approaches and techniques for navigation systems is an ongoing research topic. In this dissertation, I present a new approach and address three major navigational problems: indoor positioning, obstacle detection, and keyframe detection. The proposed approach utilizes inertial and visual sensors available on smartphones and are focused on developing: a framework for monocular visual internal odometry (VIO) to position human/object using sensor fusion and deep learning in tandem; an unsupervised algorithm to detect obstacles using sequence of visual data; and a supervised context-aware keyframe detection. The underlying technique for monocular VIO is a recurrent convolutional neural network for computing six-degree-of-freedom (6DoF) in an end-to-end fashion and an extended Kalman filter module for fine-tuning the scale parameter based on inertial observations and managing errors. I compare the results of my featureless technique with the results of conventional feature-based VIO techniques and manually-scaled results. The comparison results show that while the framework is more effective compared to featureless method and that the accuracy is improved, the accuracy of feature-based method still outperforms the proposed approach. The approach for obstacle detection is based on processing two consecutive images to detect obstacles. Conducting experiments and comparing the results of my approach with the results of two other widely used algorithms show that my algorithm performs better; 82% precision compared with 69%. In order to determine the decent frame-rate extraction from video stream, I analyzed movement patterns of camera and inferred the context of the user to generate a model associating movement anomaly with proper frames-rate extraction. The output of this model was utilized for determining the rate of keyframe extraction in visual odometry (VO). I defined and computed the effective frames for VO and experimented with and used this approach for context-aware keyframe detection. The results show that the number of frames, using inertial data to infer the decent frames, is decreased

Haptic Interaction with a Guide Robot in Zero Visibility

Search and rescue operations are often undertaken in dark and noisy environment in which rescue team must rely on haptic feedback for exploration and safe exit. However, little attention has been paid specifically to haptic sensitivity in such contexts or the possibility of enhancing communicational proficiency in the haptic mode as a life-preserving measure. The potential of root swarms for search and rescue has been shown by the Guardians project (EU, 2006-2010); however the project also showed the problem of human robot interaction in smoky (non-visibility) and noisy conditions. The REINS project (UK, 2011-2015) focused on human robot interaction in such conditions. This research is a body of work (done as a part of he REINS project) which investigates the haptic interaction of a person wit a guide robot in zero visibility. The thesis firstly reflects upon real world scenarios where people make use of the haptic sense to interact in zero visibility (such as interaction among firefighters and symbiotic relationship between visually impaired people and guide dogs). In addition, it reflects on the sensitivity and trainability of the haptic sense, to be used for the interaction. The thesis presents an analysis and evaluation of the design of a physical interface (Designed by the consortium of the REINS project) connecting the human and the robotic guide in poor visibility conditions. Finally, it lays a foundation for the design of test cases to evaluate human robot haptic interaction, taking into consideration the two aspects of the interaction, namely locomotion guidance and environmental exploration

Visual Navigation in Unknown Environments

Navigation in mobile robotics involves two tasks, keeping track of the robot's position and moving according to a control strategy. In addition, when no prior knowledge of the environment is available, the problem is even more difficult, as the robot has to build a map of its surroundings as it moves. These three problems ought to be solved in conjunction since they depend on each other. This thesis is about simultaneously controlling an autonomous vehicle, estimating its location and building the map of the environment. The main objective is to analyse the problem from a control theoretical perspective based on the EKF-SLAM implementation. The contribution of this thesis is the analysis of system's properties such as observability, controllability and stability, which allow us to propose an appropriate navigation scheme that produces well-behaved estimators, controllers, and consequently, the system as a whole. We present a steady state analysis of the SLAM problem, identifying the conditions that lead to partial observability. It is shown that the effects of partial observability appear even in the ideal linear Gaussian case. This indicates that linearisation alone is not the only cause of SLAM inconsistency, and that observability must be achieved as a prerequisite to tackling the effects of linearisation. Additionally, full observability is also shown to be necessary during diagonalisation of the covariance matrix, an approach often used to reduce the computational complexity of the SLAM algorithm, and which leads to full controllability as we show in this work.Focusing specifically on the case of a system with a single monocular camera, we present an observability analysis using the nullspace basis of the stripped observability matrix. The aim is to get a better understanding of the well known intuitive behaviour of this type of systems, such as the need for triangulation to features from different positions in order to get accurate relative pose estimates between vehicle and camera. Through characterisation the unobservable directions in monocular SLAM, we are able to identify the vehicle motions required to maximise the number of observable states in the system. When closing the control loop of the SLAM system, both the feedback controller and the estimator are shown to be asymptotically stable. Furthermore, we show that the tracking error does not influence the estimation performance of a fully observable system and viceversa, that control is not affected by the estimation. Because of this, a higher level motion strategy is required in order to enhance estimation, specially needed while performing SLAM with a single camera. Considering a real-time application, we propose a control strategy to optimise both the localisation of the vehicle and the feature map by computing the most appropriate control actions or movements. The actions are chosen in order to maximise an information theoretic metric. Simulations and real-time experiments are performed to demonstrate the feasibility of the proposed control strategy