Navigation and guidance of mobile robots towards steady or maneuvering objects (targets) is one of the most important areas of robotics that has attracted a lot of attention in recent decades. However, in most of the existing methods, both the line-of-sight angle (bearing) and the relative distance (range) are assumed to be available for navigation and guidance algorithms. There is also a relatively large body of research on navigation and guidance with bearings-only measurements. In contrast, only a few results on navigation and guidance towards an unknown target using range-only measurements have been published. Various problems of navigation, guidance, location estimation and target tracking based on range-only measurements often arise in new wireless networks related applications. Recent advances in these applications allow us to use inexpensive transponders and receivers for range-only measurements which provide information in dynamic and noisy environments without the necessity of line-of-sight. To take advantage of these sensors, algorithms must be developed for range-only navigation. The main part of this thesis is concerned with the problem of real-time navigation and guidance of Wheeled Mobile Robots (WMRs) towards an unknown stationary or moving target using range-only measurements. The range can be estimated using the signal strength and the robust extended Kalman filtering. Several similar algorithms for navigation and guidance termed Equiangular Navigation and Guidance (ENG) laws are proposed and mathematically rigorous proofs of convergence and stability of the proposed guidance laws are given. The experimental investigation into the use of range data for a WMR navigation is documented and the results and discussions on the performance of the proposed guidance strategies are presented, where a wheeled robot successfully approach a stationary or follow a maneuvering target. In order to safely navigate and reliably operate in populated environments, ENG is then modified into Augmented-ENG (AENG), which enables the robot to approach a stationary target or follow an unpredictable maneuvering object in an unknown environment, while keeping a safe distance from the target, and simultaneously preserving a safety margin from the obstacles. Furthermore, we propose and experimentally investigate a new biologically inspired method for local obstacle avoidance and give the mathematically rigorous proof of the idea. In order for the robot to avoid collision and bypass the enroute obstacles in this method, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The proposed idea is combined with the ENG law, which leads to a reliable and fast long-range navigation. The performance of both navigation strategy and local obstacle avoidance techniques are confirmed with computer simulations and several experiments with ActivMedia Pioneer 3-DX wheeled robots. The second part of the thesis investigates some challenging problems in the area of wheeled robot navigation. We first address the problem of bearing-only guidance of an autonomous vehicle following a moving target with smaller minimum turning radius compared to that of the follower and propose a simple and constructive navigation law. In compliance with the increasing research on decentralized control laws for groups of mobile autonomous robots, we consider the problems of decentralized navigation of network of WMRs with limited communication and decentralized stabilization of formation of WMRs. New control laws are presented and simulation results are provided to illustrate the control laws and their applications.

Drones or UAVs are quickly becoming a bigger part of today's society. Delivery servicesand transportation are elds were big development is being done. For the UAVs to beable to perform its given tasks safely more and more sensors are implemented.This report covers the development and implementation of a sensor system to helpan UAV to keep a xed altitude and provide proximity measurements of the environ-ment to avoid obstacles. The system is build around the ATmega328P microprocessorand uses I2C to communicate with the sensors. Measurements are ltered and pub-lished into ROS where the autopilot can reach the measurements and make decisionsbased on the readings. Additionally, simple algorithms to avoid obstacles have beenimplemented and simulated in the simulation software Gazebo. The altitude controlsystem which has been the main focus of the project has been implemented with goodresults in both simulation and real ight tests. The system will be used in a competi-tion held in Arizona, USA where the project team together with two other project willcompete in the prestigious CPS-challenge.

The ability to navigate with limited vision is a skill that is often employed in our daily lives. Navigating without vision to a remembered target has previously been studied. However, not much is known about the attention required to perform blind navigation. We examined the effect of aging and presence of obstacles on the attentional demands of blind navigation. We evaluated reaction time, navigation errors and average walking speed in an 8 meter walking path, with or without obstacles, in the absence of vision. Results showed that older participants had increased reaction time and increased linear distance travelled as opposed to young participants, that obstacles increased reaction time and decreased average walking speed in all participants, and that emitting the reaction time stimulus early in the trial increased the linear distance travelled. Interpretation of the results suggests that aging and presence of obstacles augments the attentional demands of blind navigation.

Obstacle avoidance is one of the core problems in the field of autonomous navigation. An obstacle avoidance approach is developed for the navigation task of a reconfigurable multi-robot system named STORM, which stands for Self-configurable and Transformable Omni-Directional Robotic Modules. Various mathematical models have been developed in previous work in this field to avoid collision for such robots. In this work, the proposed collision avoidance algorithm is trained via Deep Reinforcement Learning, which enables the robot to learn by itself from its experiences, and then fit a mathematical model by updating the parameters of a neural network. The trained neural network architecture is capable of choosing an action directly based on the input sensor data using the trained neural network architecture. A virtual STORM locomotion module was trained to explore a Gazebo simulation environment without collision, using the proposed collision avoidance strategies based on DRL. The mathematical model of the avoidance algorithm was derived from the simulation and then applied to the prototype of the locomotion module and validated via experiments. Universal software architecture was also designed for the STORM modules. The software architecture has extensible and reusable features that improve the design efficiency and enable parallel development. / Master of Science / In this thesis, an obstacle avoidance approach is described to enable autonomous navigation of a reconfigurable multi-robot system, STORM. The Self-configurable and Transformable Omni-Directional Robotic Modules (STORM) is a novel approach towards heterogeneous swarm robotics. The system has two types of robotic modules, namely the locomotion module and the manipulation module. Each module is able to navigate and perform tasks independently. In addition, the systems are designed to autonomously dock together to perform tasks that the modules individually are unable to accomplish. The proposed obstacle avoidance approach is designed for the modules of STORM, but can be applied to mobile robots in general. In contrast to the existing collision avoidance approaches, the proposed algorithm was trained via deep reinforcement learning (DRL). This enables the robot to learn by itself from its experiences, and then fit a mathematical model by updating the parameters of a neural network. In order to avoid damage to the real robot during the learning phase, a virtual robot was trained inside a Gazebo simulation environment with obstacles. The mathematical model for the collision avoidance strategy obtained through DRL was then validated on a locomotion module prototype of STORM. This thesis also introduces the overall STORM architecture and provides a brief overview of the generalized software architecture designed for the STORM modules. The software architecture has expandable and reusable features that apply well to the swarm architecture while allowing for design efficiency and parallel development.

Sight is essential in our daily tasks. Compensatory senses have been used for centuries by visually impaired individuals to navigate independently. The help of technology can minimize some challenges for visually impaired individuals. Assistive navigation technologies facilitate the pathfinding and tracing in indoor scenarios. Different modules are added to assistive navigation technologies to warn about the obstacles not only on the ground but about hanging objects. In this work, we attempt to explore new methods to assist visually impaired individuals in navigating independently in an indoor scenario. We employed a location estimation algorithm based on the fingerprinting method to estimate the initial location of the user. We mitigate the error of estimation with particle filter. The shortest path has been calculated with an A* algorithm. To provide the user with an accident-free experiment, we employed an obstacle avoidance algorithm capable of warning the users about the potential hazards. Finally, to provide an effective means of communication with the user, we employed text-to-speech and speech recognition algorithms. The main contribution of this work is to glue these modules together efficiently and affordably.

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The use of autonomous aerial vehicles (UAVs) to explore unknown environments is a growing field of research; of particular interest is locating a target that emits a signal within an unknown environment. Several physical processes produce scalar signals that attenuate with distance from their source, such as chemical, biological, electromagnetic, thermal, and radar signals. The natural decay of the signal with increasing distance enables a gradient ascent method to be used to navigate toward the target. The UAVs navigate around obstacles whose positions are initially unknown; a hybrid controller comprised of overlapping control modes enables robust obstacle avoidance in the presence of exogenous inputs by precluding topological obstructions. Limitations of a distributed gradient augmentation approach to obstacle avoidance are discussed, and an alternative algorithm is presented which retains the robustness of the hybrid control while leveraging local obstacle position information to improve non-collision reliability. A heterogeneous swarm of multirotors demonstrates the target locating problem, sharing information over a multicast wireless private network in a fully distributed manner to form an estimate of the signal's gradient, informing the direction of travel toward the target. The UAVs navigate around obstacles, showcasing both algorithms developed for obstacle avoidance. Each UAV performs its own target seeking and obstacle avoidance calculations in a distributed architecture, receiving position data from an OptiTrack motion capture system, illustrating the applicability of the control law to real world challenges (e.g., unsynchronized clocks among different UAVs, limited computational power, and communication latency). Experimental and theoretical results are compared. / Master of Science / In this project, a new method for locating a target using a swarm of unmanned drones in an unknown environment is developed and demonstrated. The drones measure a signal such as a beacon that is being emitted by the target of interest, sharing their measurement information with the other drones in the swarm. The magnitude of the signal increases as the drones move toward the target, allowing the drones to estimate the direction to the target by comparing their measurements with the measurements collected by other drones. While seeking the target in this manner, the drones detect obstacles that they need to avoid. An issue that arises in obstacle avoidance is that drones can get stuck in front of an obstacle if they are unable to decide which direction to travel; in this work, the decision process is managed by combining two control modes that correspond to the two direction options available, using a robust switching algorithm to select which mode to use for each obstacle. This work extends the approach used in literature to include multiple obstacles and allow obstacles to be detected dynamically, enabling the drones to navigate through an unknown environment as they locate the target. The algorithms are demonstrated on unmanned drones in the VT SpaceDrones test facility, illustrating the capabilities and effectiveness of the methods presented in a series of scenarios.

This dissertation aims to address how a change in landing substrate may change landing kinematics. To examine this possibility, mallards (Anas playtrhynchos) were used as a study species and 177 water landings were recorded through the use of two camera systems with photogrammetric capabilities. This enabled the landing trajectory and landing transition kinematics to be tracked in three dimensions. From the resulting position data three questions were pursued. Do mallards regulate landing kinematics through a ̇-constant strategy? With what kinematics do mallards land on water? Do landing kinematics respond to external factors, such as an obstacle to landing? Chapter 2 assesses the presence of a ̇-constant regulatory strategy and compares the implementation to other landing behaviors. Chapter 3 examines the variation observed in the landing kinematics of mallards, identifies the primary kinematic drivers of that variation, and detects differences in kinematic profile. Chapter 4 inspects the landing kinematics combined with the positions of all other waterfowl in the vicinity to test for the presence of obstacle avoidance behavior. / Doctor of Philosophy / Control of landing is an important ability for any flying animal. However, with the exception of perch landing, we know very little about how birds and other flyers land on a variety of different surfaces. Here, we aim to extend our knowledge in this area by focusing on how mallard ducks land on water. This dissertation addresses the following questions. Do mallards regulate landing speed and trajectory the same way as pigeons? At what speeds, angles, and postures do mallards land on water? Can mallards adjust landing behavior to avoid collisions with other birds on the water surface? Chapter 2 determines how mallards regulate landings and how it is similar and different from pigeons and several other flyers. Chapter 3 describes the speeds, angles, and postures used by mallards to land on water. In addition, this chapter finds evidence for at least two different categories of landing performed by mallards. Chapter 4 provides evidence that mallards avoid situations in which a collision with another bird is likely. However, it is unclear if this is an active choice made by the mallard or due to other circumstances related to the landing behavior. Overall, this dissertation illustrates how the landing behavior of mallards is similar to what has been documented in other animals. However there are significant differences such as higher impact speeds, and shallower angles. Both of which are likely related to the ability of water to absorb a greater amount of the impact forces than a perch or the ground would.

This work focuses on the development and implementation of an autonomous path planning and obstacle avoidance algorithm for an autonomous surface vehicle (ASV) in a riverine environment. The algorithm effectively handles trap situations, which occur when the river bends away from the destination. In addition, the algorithm uses real-time sensor feedback to avoid obstacles. A general global route is proposed based on an a priori shoreline map. Then, local paths are calculated considering both the a priori data and measurements received from an obstacle sensor. These paths roughly follow the global path. The algorithm was tested on an ASV equipped with basic navigational sensors and an omnidirectional camera for obstacle detection, and experimentation verified its effectiveness. / Master of Science