Active Vision-Based Guidance with a Mobile Device for People with Visual Impairments

The aim of this research is to determine whether an active-vision system with a human-in-the-loop can be implemented to guide a user with visual impairments in finding a target object. Active vision techniques have successfully been applied to various electro-mechanical object search and exploration systems to boost their effectiveness at a given task. However, despite the potential of intelligent visual sensor arrays to enhance a user’s vision capabilities and alleviate some of the impacts that visual deficiencies have on their day-to-day lives, active vision techniques with human-in-the-loop remains an open research topic. In this thesis, an active guidance system is presented, which uses visual input from an object detector and an initial understanding of a typical room layout to generate navigation cues that assist a user with visual impairments in finding a target object. A complete guidance system prototype is implemented, along with a new audio-based interface and a state-of-the-art object detector, onto a mobile device and evaluated with a set of users in real environments. The results show that an active guidance approach performs well compared to other unguided solutions. This research highlights the potential benefits of the proposed active guidance controller and audio interface, which could enhance current vision-based guidance systems and travel aids for people with visual impairments

Data-driven Policy Transfer with Imprecise Perception Simulation

The paper presents a complete pipeline for learning continuous motion control policies for a mobile robot when only a non-differentiable physics simulator of robot-terrain interactions is available. The multi-modal state estimation of the robot is also complex and difficult to simulate, so we simultaneously learn a generative model which refines simulator outputs. We propose a coarse-to-fine learning paradigm, where the coarse motion planning is alternated with imitation learning and policy transfer to the real robot. The policy is jointly optimized with the generative model. We evaluate the method on a real-world platform in a batch of experiments.Comment: Submitted to IROS 2018 with RAL optio

Automation and robotics for the Space Exploration Initiative: Results from Project Outreach

A total of 52 submissions were received in the Automation and Robotics (A&R) area during Project Outreach. About half of the submissions (24) contained concepts that were judged to have high utility for the Space Exploration Initiative (SEI) and were analyzed further by the robotics panel. These 24 submissions are analyzed here. Three types of robots were proposed in the high scoring submissions: structured task robots (STRs), teleoperated robots (TORs), and surface exploration robots. Several advanced TOR control interface technologies were proposed in the submissions. Many A&R concepts or potential standards were presented or alluded to by the submitters, but few specific technologies or systems were suggested

The New Way of War: Is There A Duty to Use Drones?

Part I of this Article briefly describes the newest battlespace occupants. Robotic systems have been taking active part in combat. They now inhabit the air, the land, and the sea. They carry out missions ranging from surveillance and bomb disposal to “destroy and disable.” Part II examines the relevant principles of LOAC. It argues that drones are not, per se, unlawful under LOAC. Rather, the critical question is the same for drones as for other types of weapons, i.e., whether the specific use of the weapon complies with LOAC. In this context, the weapon must be deployed in accordance with LOAC’s fundamental principles of humanity, proportionality, distinction, taking precautions, and military necessity. Even if a specific type of weapon is not unlawful per se (or has not been specifically prohibited by particular treaties), it may not be used improperly, e.g., in a manner that would run afoul of these principles. Part III applies the principles of LOAC to drones. First, it analyzes the general trajectories of the development of new weapons throughout human history, which has involved trading off between three main considerations, namely distance, accuracy, and lethality. Second, it examines the rise of precision-guided munitions as an attempt to balance these three considerations, increasing military efficiency while minimizing harm to civilians and civilian objects. Part IV discusses the ability of drones to combine both remote exercise of force and high accuracy to reduce lethality. Part IV also closely examines both the promised benefits that the use of drones may bring to battlespace and the challenges to their deployment. Part V returns to the question of whether states and their military commanders have an obligation to use drones in the context of an armed conflict. It argues that although there are no treaties that deal specifically with the use of drones in armed conflict and no customary norms obligating the use of drones, such a duty may be derived from the cardinal principles of the law of armed conflict. It suggests that such an interpretation is merited if we accept that drones offer the possibility of a more humane war by combining remote and accurate use of force to reduce lethality among both friendly forces and innocent civilians. Part V concludes by setting out further challenges that ought to receive careful attention in developing and elaborating on the obligation to use drones in the battlefield

Decision tree learning for intelligent mobile robot navigation

The replication of human intelligence, learning and reasoning by means of computer algorithms is termed Artificial Intelligence (Al) and the interaction of such algorithms with the physical world can be achieved using robotics. The work described in this thesis investigates the applications of concept learning (an approach which takes its inspiration from biological motivations and from survival instincts in particular) to robot control and path planning. The methodology of concept learning has been applied using learning decision trees (DTs) which induce domain knowledge from a finite set of training vectors which in turn describe systematically a physical entity and are used to train a robot to learn new concepts and to adapt its behaviour. To achieve behaviour learning, this work introduces the novel approach of hierarchical learning and knowledge decomposition to the frame of the reactive robot architecture. Following the analogy with survival instincts, the robot is first taught how to survive in very simple and homogeneous environments, namely a world without any disturbances or any kind of "hostility". Once this simple behaviour, named a primitive, has been established, the robot is trained to adapt new knowledge to cope with increasingly complex environments by adding further worlds to its existing knowledge. The repertoire of the robot behaviours in the form of symbolic knowledge is retained in a hierarchy of clustered decision trees (DTs) accommodating a number of primitives. To classify robot perceptions, control rules are synthesised using symbolic knowledge derived from searching the hierarchy of DTs. A second novel concept is introduced, namely that of multi-dimensional fuzzy associative memories (MDFAMs). These are clustered fuzzy decision trees (FDTs) which are trained locally and accommodate specific perceptual knowledge. Fuzzy logic is incorporated to deal with inherent noise in sensory data and to merge conflicting behaviours of the DTs. In this thesis, the feasibility of the developed techniques is illustrated in the robot applications, their benefits and drawbacks are discussed

Accelerating decision making under partial observability using learned action priors

Thesis (M.Sc.)--University of the Witwatersrand, Faculty of Science, School of Computer Science and Applied Mathematics, 2017.Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical framework allowing a robot to reason about the consequences of actions and observations with respect to the agent's limited perception of its environment. They allow an agent to plan and act optimally in uncertain environments. Although they have been successfully applied to various robotic tasks, they are infamous for their high computational cost. This thesis demonstrates the use of knowledge transfer, learned from previous experiences, to accelerate the learning of POMDP tasks. We propose that in order for an agent to learn to solve these tasks quicker, it must be able to generalise from past behaviours and transfer knowledge, learned from solving multiple tasks, between di erent circumstances. We present a method for accelerating this learning process by learning the statistics of action choices over the lifetime of an agent, known as action priors. Action priors specify the usefulness of actions in situations and allow us to bias exploration, which in turn improves the performance of the learning process. Using navigation domains, we study the degree to which transferring knowledge between tasks in this way results in a considerable speed up in solution times. This thesis therefore makes the following contributions. We provide an algorithm for learning action priors from a set of approximately optimal value functions and two approaches with which a prior knowledge over actions can be used in a POMDP context. As such, we show that considerable gains in speed can be achieved in learning subsequent tasks using prior knowledge rather than learning from scratch. Learning with action priors can particularly be useful in reducing the cost of exploration in the early stages of the learning process as the priors can act as mechanism that allows the agent to select more useful actions given particular circumstances. Thus, we demonstrate how the initial losses associated with unguided exploration can be alleviated through the use of action priors which allow for safer exploration. Additionally, we illustrate that action priors can also improve the computation speeds of learning feasible policies in a shorter period of time.MT201

Development and Testing of a Steerable Cruciform Parachute System

Title from PDF of title page viewed June 18, 2018Thesis advisor: Travis FieldsVitaIncludes bibliographical references (pages 93-99)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2018This thesis focuses on the development of a parachute payload system which is capable of precision aerial delivery yet only represents a modest cost increase over ballistic unguided systems. In order to develop such a system, first a canopy is selected. The canopy should be simple and inexpensive to make; in this case a cruciform canopy was selected because this design is material efficient and requires far less labor to manufacture compared to parafoil parachutes. Next some method of stabilizing that canopy during flight must be proposed. In this case, the system heading is to be stabilized via a single actuator by asymmetric deflection of the leading edge of one canopy panel. At this stage in the development, a controller must be designed and implemented which stabilizes the system in the proposed way. Outdoor flight testing is the gold standard of parachute testing methodology since it offers the most realistic flight conditions. However, the unmeasured wind disturbances encountered in outdoor flight testing can confound results and interfere with repeatability of experiments. The first experiment explained in this thesis revolves around the testing of a steer able cruciform parachute system using a vertical wind tunnel. The primary goal of the experiment was to develop a heading stabilizing controller. Additionally, a closed-loop system model was identified and a technique was developed for estimating canopy glide ratio (GR). The vertical wind tunnel testing methodology is far faster and less expensive than the outdoor flight testing which would be needed to accomplish the same goals. After proving that a system can be steered via the proposed methodology, the next stage in the developing of a precision guided vehicle is to demonstrate that the stabilization technique is viable. This is accomplished in both outdoor flight testing and a simulation based on the closed-loop model identified earlier. Furthermore, the precision navigation potential of the system must be demonstrated; specifically, the system must be capable of arriving closer to the desired impact point on the ground than an unguided system dropped under the same conditions. The work described in this thesis has advanced the development of the steerable cruciform parachute system beyond the point of simply being a feasibility demonstrator. The vertical wind tunnel experiments demonstrated that the system heading could be stabilized and subsequent navigation experiments demonstrated that the system outperforms an unguided system during real drops. The work done to compare the effectiveness of different navigation strategies in a simulated environment represents the beginning of the next stage in the development of the parachute system. This next stage involves refinement and performance improvements of the existing platform through engineering design in order to advance the technical readiness level of the project.Introduction -- Literature review -- Vertical wind tunnel experiment -- Investigation of navigation strategies -- Conclusions -- Appendix A. Unmanned aerial systems and parachute release mechanisms -- Appendix B. Aerial guidance unit redesig