Aerial Field Robotics

Aerial field robotics research represents the domain of study that aims to equip unmanned aerial vehicles - and as it pertains to this chapter, specifically Micro Aerial Vehicles (MAVs)- with the ability to operate in real-life environments that present challenges to safe navigation. We present the key elements of autonomy for MAVs that are resilient to collisions and sensing degradation, while operating under constrained computational resources. We overview aspects of the state of the art, outline bottlenecks to resilient navigation autonomy, and overview the field-readiness of MAVs. We conclude with notable contributions and discuss considerations for future research that are essential for resilience in aerial robotics.Comment: Accepted in the Encyclopedia of Robotics, Springe

Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team's driving log data to determine the impact of competitive autonomous driving on the traffic environment

A Review of Sensor Technologies for Perception in Automated Driving

After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show future market trends in sensors technologies for Automated Vehicles.This work has been partly supported by ECSEL Project ENABLE- S3 (with grant agreement number 692455-2), by the Spanish Government through CICYT projects (TRA2015- 63708-R and TRA2016-78886-C3-1-R)

Advantages and challenges of unmanned aerial vehicle autonomy in the Postheroic age

Over the past decade, unmanned aerial vehicles (UAVs) have revolutionized how the U.S. engages elusive militants in low-intensity conflicts by allowing the U.S. to project continuous military power without risking combat casualties. While UAV usage promises additional tactical advantages in future conflicts, little agreement exists regarding a strategic vision for UAV research and development, necessary for the U.S. to allocate limited resources among UAV development programs that address national security objectives. The present research makes the case for a future UAV technology evolutionary path leading to fully autonomous intelligence, surveillance, and reconnaissance (ISR)/strike UAV systems for the United States Air Force that are capable of sensing their environments through multiple modalities, recognizing patterns, and executing appropriate actions in response to their real-time analyses. The thesis addresses enabling technology inroads stemming from major improvements in our understanding of human neural circuitry that promise to enable innovations in the artificial intelligence needed to achieve autonomous system function. Arguments are based on projected military and economic benefits of autonomous systems and extend the historical model established by the CIA\u27s successful UAV program to unconventional warfare (UW) conflicts that the U.S. Air Force finds itself ill-equipped to handle. Counter-arguments are addressed relating to uncontrolled lethal technology, conflict initiation thresholds, and the vulnerability of overreliance on high-technology systems. In making the case for fully automated UAV technology, research provides a strategic future vision for autonomous UAV usage by highlighting the important interaction of artificial intelligence, “smart” wide-area sensors, and cooperative micro UAVs

Mind the Gap: Developments in Autonomous Driving Research and the Sustainability Challenge

Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modeling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions

Academic competitions

Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in terms of accessing and participating in the shaping of research fields. Competitions can be traced back for centuries and their achievements have had great influence in our modern world. Recently, they (re)gained popularity, with the overwhelming amounts of data that is being generated in different domains, as well as the need of pushing the barriers of existing methods, and available tools to handle such data. This chapter provides a survey of academic challenges in the context of machine learning and related fields. We review the most influential competitions in the last few years and analyze challenges per area of knowledge. The aims of scientific challenges, their goals, major achievements and expectations for the next few years are reviewed

Connected And Autonomous Vehicles: Implications For Policy And Practice In City And Transportation Planning

Vehicular transportation is undergoing a technological change. Cars are being automated, which have significant implications for governments. Autonomous Vehicles (AVs) and Connected and Autonomous Vehicles (CAVs) can have significant benefits such as improved overall roadside safety and efficiency however, there may also be negative effects as well such as increased sprawl and social inequity. In Ontario, AV testing on public roads has been conducted under O. Reg. 306/15, which has also helped to establish Ontario as a leader of innovation in Canada. Before CAVs can be mass deployed in Ontario and Canada at large however, a number of barriers will need to be addressed such as legislation, infrastructure and cooperation between municipalities, and between municipalities and the automotive industry. Recommendations for municipal and provincial governments are provided