Particle Swarm Optimization Based Source Seeking

Signal source seeking using autonomous vehicles is a complex problem. The complexity increases manifold when signal intensities captured by physical sensors onboard are noisy and unreliable. Added to the fact that signal strength decays with distance, noisy environments make it extremely difficult to describe and model a decay function. This paper addresses our work with seeking maximum signal strength in a continuous electromagnetic signal source with mobile robots, using Particle Swarm Optimization (PSO). A one to one correspondence with swarm members in a PSO and physical Mobile robots is established and the positions of the robots are iteratively updated as the PSO algorithm proceeds forward. Since physical robots are responsive to swarm position updates, modifications were required to implement the interaction between real robots and the PSO algorithm. The development of modifications necessary to implement PSO on mobile robots, and strategies to adapt to real life environments such as obstacles and collision objects are presented in this paper. Our findings are also validated using experimental testbeds.Comment: 13 pages, 12 figure

Full Potential of Future Robotaxis Achievable with Trip-Based Subsidies and Fees Applied to the For-Hire Vehicles of Today

As described by Grush and Niles in their textbook, The End of Driving: Transportation Systems and Public Policy Planning for Autonomous Vehicles, there are two distinct market states for the future of automobility as vehicles become increasingly automated. The first, Market-1, is comprised of all vehicles that are manufactured and sold to private owners and used as household vehicles. This private consumer fleet will—through automated driver assistance systems (ADAS)—be increasingly capable of hands-off operation, even self-driving in certain environments such as limited-access expressways. The second category, Market-2, represents all the vehicles made expressly for the service market, i.e., roboshuttles and robotaxis, meant to be eventually driverless in prepared, defined areas and streets. Ford, GM, Lyft, Uber, Waymo, and dozens of other companies assert that they are preparing vehicles for Market-2. The main thesis in this perspective is that a productive, efficient system of on-demand Market-2 mobility can evolve from incentive-based governance—here termed “harmonization management.” This approach strikes a contrast with rigid regulation of a style seen with big city taxicabs and based on using constrained service classifications or per-vehicle medallion approaches. This essay recommends that transportation authorities set up systems of robust pricing signals—incentives and fees—delivered through a universal, mandatory system providing efficient, equitable distribution of these signals

An Autonomous Surface Vehicle for Long Term Operations

Environmental monitoring of marine environments presents several challenges: the harshness of the environment, the often remote location, and most importantly, the vast area it covers. Manual operations are time consuming, often dangerous, and labor intensive. Operations from oceanographic vessels are costly and limited to open seas and generally deeper bodies of water. In addition, with lake, river, and ocean shoreline being a finite resource, waterfront property presents an ever increasing valued commodity, requiring exploration and continued monitoring of remote waterways. In order to efficiently explore and monitor currently known marine environments as well as reach and explore remote areas of interest, we present a design of an autonomous surface vehicle (ASV) with the power to cover large areas, the payload capacity to carry sufficient power and sensor equipment, and enough fuel to remain on task for extended periods. An analysis of the design and a discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto

3-D Velocity Regulation for Nonholonomic Source Seeking Without Position Measurement

We consider a three-dimensional problem of steering a nonholonomic vehicle to seek an unknown source of a spatially distributed signal field without any position measurement. In the literature, there exists an extremum seeking-based strategy under a constant forward velocity and tunable pitch and yaw velocities. Obviously, the vehicle with a constant forward velocity may exhibit certain overshoots in the seeking process and can not slow down even it approaches the source. To resolve this undesired behavior, this paper proposes a regulation strategy for the forward velocity along with the pitch and yaw velocities. Under such a strategy, the vehicle slows down near the source and stays within a small area as if it comes to a full stop, and controllers for angular velocities become succinct. We prove the local exponential convergence via the averaging technique. Finally, the theoretical results are illustrated with simulations.Comment: submitted to IEEE TCST;12 pages, 10 figure

Autonomous Planning and Mapping for the Characterization of Gamma Contaminated Environments

The past $100$ years of research and development in the fields of nuclear power, weapons, and industrial radiation applications have imbibed regions across the world with facilities and terrain which is contaminated with radioactive material. Such locations can pose significant hazards to human health, thus requiring vigilant monitoring and mitigation efforts. The use of autonomous robots is well suited to this task. Motivated by this fact, this work contributes a holistic perspective on the deployment, design, and use of autonomous robots for the characterization of radioactively contaminated environments. The set of developments presented in this dissertation incorporate principles of gamma radiation detection and measurement, techniques for mapping and localizing a variety of radioactive sources, path planning strategies tailored to both ground and aerial platforms, as well as prototype systems implementing methods for perception and navigation in dirty, dangerous, and degraded conditions. Specifically, Chapter \ref{chap:intro} presents the motivation behind this work, including its practical application, as well as a brief description of the approach utilized to accomplish environmental radiation characterization. Chapter \ref{chap:contrib} presents a detailed overview of the presented radiation mapping contributions and associated publications in addition to a brief note on other synergistic contributions made towards enabling autonomy in the perceptually degraded environments associated in particular with waste decommissioning facilities. Subsequently the core contributions of this thesis are presented in detail. Chapter \ref{chap:single_source} presents a method for autonomous single source localization using an aerial robot, alongside details regarding principles of radiation measurement and detection. Chapter \ref{chap:radbot} describes a technique developed to map distributed radiation fields in 2D using a ground platform, while Chapter \ref{chap:radmf} extends the work to perform the mapping task in 3D using a collision tolerant micro aerial vehicle. Subsequently, Chapter \ref{chap:auro} presents autonomous distributed 3D radiation mapping coupled with an intelligent path planning algorithm tailored to source seeking behaviors in confined environments. Finally, conclusions and an outlook for future research are discussed in Chapter \ref{chap:conclusions}.Overall, this dissertation contributes a body of work enabling autonomous radiological surveying in challenging conditions, demonstrating robust functionality through a series of field experiments using real radiation sources. Each of the presented methods is associated with a tested and reliable robotic system purpose-built for its designated task. This combination of performance robotic hardware demonstrating novel autonomous functionality in realistic use-case scenarios showcases the applicability and dependability of the presented systems and methods

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Vehicle Motion Planning Using Stream Functions

Borrowing a concept from hydrodynamic analysis, this paper presents stream functions which satisfy Laplace's equation as a local-minima free method for producing potential-field based navigation functions in two dimensions. These functions generate smoother paths (i.e. more suited to aircraft-like vehicles) than previous methods. A method is developed for constructing analytic stream functions to produce arbitrary vehicle behaviors while avoiding obstacles, and an exact solution for the case of a single uniformly moving obstacle is presented. The effects of introducing multiple obstacles are discussed and current work in this direction is detailed. Experimental results generated on the Cornell RoboFlag testbed are presented and discussed, as well as related work applying these methods to path planning for unmanned air vehicles