Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

The augmented reality framework : an approach to the rapid creation of mixed reality environments and testing scenarios

Debugging errors during real-world testing of remote platforms can be time consuming and expensive when the remote environment is inaccessible and hazardous such as deep-sea. Pre-real world testing facilities, such as Hardware-In-the-Loop (HIL), are often not available due to the time and expense necessary to create them. Testing facilities tend to be monolithic in structure and thus inflexible making complete redesign necessary for slightly different uses. Redesign is simpler in the short term than creating the required architecture for a generic facility. This leads to expensive facilities, due to reinvention of the wheel, or worse, no testing facilities. Without adequate pre-real world testing, integration errors can go undetected until real world testing where they are more costly to diagnose and rectify, e.g. especially when developing Unmanned Underwater Vehicles (UUVs). This thesis introduces a novel framework, the Augmented Reality Framework (ARF), for rapid construction of virtual environments for Augmented Reality tasks such as Pure Simulation, HIL, Hybrid Simulation and real world testing. ARF’s architecture is based on JavaBeans and is therefore inherently generic, flexible and extendable. The aim is to increase the performance of constructing, reconfiguring and extending virtual environments, and consequently enable more mature and stable systems to be developed in less time due to previously undetectable faults being diagnosed earlier in the pre-real-world testing phase. This is only achievable if test harnesses can be created quickly and easily, which in turn allows the developer to visualise more system feedback making faults easier to spot. Early fault detection and less wasted real world testing leads to a more mature, stable and less expensive system. ARF provides guidance on how to connect and configure user made components, allowing for rapid prototyping and complex virtual environments to be created quickly and easily. In essence, ARF tries to provide intuitive construction guidance which is similar in nature to LEGOR pieces which can be so easily connected to form useful configurations. ARF is demonstrated through case studies which show the flexibility and applicability of ARF to testing techniques such as HIL for UUVs. In addition, an informal study was carried out to asses the performance increases attributable to ARF’s core concepts. In comparison to classical programming methods ARF’s average performance increase was close to 200%. The study showed that ARF was incredibly intuitive since the test subjects were novices in ARF but experts in programming. ARF provides key contributions in the field of HIL testing of remote systems by providing more accessible facilities that allow new or modified testing scenarios to be created where it might not have been feasible to do so before. In turn this leads to early detection of faults which in some cases would not have ever been detected before

Adaptive sampling in autonomous marine sensor networks

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2006In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control in complex environments with multiple constraints, and an approach to cooperative robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches. The mobility of the sensor platforms is a key advantage of this strategy, allowing dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time. Experimental results are presented for a 2-D target tracking application in which fully autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate. In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based control for dealing with uncertainty in complex situations in marine sensor networks.Supported by the Office of Naval Research, with a 3-year National Defense Science and Engineering Grant Fellowship and research assistantships through the Generic Ocean Array Technology Sonar (GOATS) project, contract N00014-97-1-0202 and contract N00014-05-G-0106 Delivery Order 008, PLUSNET: Persistent Littoral Undersea Surveillance Network