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Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment

arXiv.org Machine Learning

Unmanned Aerial Systems (UAS) are being increasingly deployed for commercial, civilian, and military applications. The current UAS state-of-the-art still depends on a remote human controller with robust wireless links to perform several of these applications. The lack of autonomy restricts the domains of application and tasks for which a UAS can be deployed. Enabling autonomy and intelligence to the UAS will help overcome this hurdle and expand its use improving safety and efficiency. The exponential increase in computing resources and the availability of large amount of data in this digital era has led to the resurgence of machine learning from its last winter. Therefore, in this chapter, we discuss how some of the advances in machine learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS. We first begin motivating this chapter by discussing the application, challenges, and opportunities of the current UAS in the introductory section. We then provide an overview of some of the key deep learning and reinforcement learning techniques discussed throughout this chapter. A key area of focus that will be essential to enable autonomy to UAS is computer vision. Accordingly, we discuss how deep learning approaches have been used to accomplish some of the basic tasks that contribute to providing UAS autonomy. Then we discuss how reinforcement learning is explored for using this information to provide autonomous control and navigation for UAS. Next, we provide the reader with directions to choose appropriate simulation suites and hardware platforms that will help to rapidly prototype novel machine learning based solutions for UAS. We additionally discuss the open problems and challenges pertaining to each aspect of developing autonomous UAS solutions to shine light on potential research areas.


Applications of blockchain in unmanned aerial vehicles: A review

#artificialintelligence

The recent advancement in Unmanned Aerial Vehicles (UAVs) in terms of manufacturing processes, and communication and networking technology has led to a rise in their usage in civilian and commercial applications. The regulations of the Federal Aviation Administration (FAA) in the US had earlier limited the usage of UAVs to military applications. However more recently, the FAA has outlined new enforcement that will also expand the usage of UAVs in civilian and commercial applications. Due to being deployed in open atmosphere, UAVs are vulnerable to being lost, destroyed or physically hijacked. With the UAV technology becoming ubiquitous, various issues in UAV networks such as intra-UAV communication, UAV security, air data security, data storage and management, etc. need to be addressed.


Towards a Framework for Certification of Reliable Autonomous Systems

arXiv.org Artificial Intelligence

The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.


Ten Ways the Precautionary Principle Undermines Progress in Artificial Intelligence

#artificialintelligence

Artificial intelligence (AI) has the potential to deliver significant social and economic benefits, including reducing accidental deaths and injuries, making new scientific discoveries, and increasing productivity.[1] However, an increasing number of activists, scholars, and pundits see AI as inherently risky, creating substantial negative impacts such as eliminating jobs, eroding personal liberties, and reducing human intelligence.[2] Some even see AI as dehumanizing, dystopian, and a threat to humanity.[3] As such, the world is dividing into two camps regarding AI: those who support the technology and those who oppose it. Unfortunately, the latter camp is increasingly dominating AI discussions, not just in the United States, but in many nations around the world. There should be no doubt that nations that tilt toward fear rather than optimism are more likely to put in place policies and practices that limit AI development and adoption, which will hurt their economic growth, social ...


A Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts

arXiv.org Machine Learning

Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the National Aerospace (NAS) is a complex problem which is required to be addressed by simulation studies before allowing the routine access of UAS into the NAS. This thesis focuses on providing 2D and 3D simulation frameworks using a game theoretical methodology to evaluate integration concepts in scenarios where manned and unmanned air vehicles co-exist. The fundamental gap in the literature is that the models of interaction between manned and unmanned vehicles are insufficient: a) they assume that pilot behavior is known a priori and b) they disregard decision making processes. The contribution of this work is to propose a modeling framework, in which, human pilot reactions are modeled using reinforcement learning and a game theoretical concept called level-k reasoning to fill this gap. The level-k reasoning concept is based on the assumption that humans have various levels of decision making. Reinforcement learning is a mathematical learning method that is rooted in human learning. In this work, a classical and an approximate reinforcement learning (Neural Fitted Q Iteration) methods are used to model time-extended decisions of pilots with 2D and 3D maneuvers. An analysis of UAS integration is conducted using example scenarios in the presence of manned aircraft and fully autonomous UAS equipped with sense and avoid algorithms.


Apple, Amazon snubbed in race to bring drones to the skies as feds approve 10 testing projects

Daily Mail - Science & tech

Apple and Amazon were passed over in a program spearheaded by the Trump administration that would have given them a greater say in how the drone industry is regulated. On Wednesday, the US Transportation Department announced 10 winning drone pilot projects that will help more unmanned aerial vehicles take to the skies. Among the winners were Silicon Valley tech giants Google, Intel, Qualcomm and Microsoft. However, Transportation Secretary Elaine Chao said there are'no losers' and she thinks dozens of the applicants not chosen could be greenlighted by the FAA in the coming months. Selected winners will be able to conduct experimental drone flights that are beyond the rules outlined by the Federal Aviation Administration (FAA).


Drones could soon deliver packages right to your doorstep

Daily Mail - Science & tech

Don't be surprised if you see a drone outside on your doorstep this summer. Federal regulators want to begin using drones for'limited package deliveries' as soon as within the next few months, according to the Wall Street Journal. Officials have been working with Silicon Valley tech giants and aerospace companies to develop proposals, rewrite regulations and address safety concerns, as part of an effort to make the technology a reality. A drone delivers an Amazon package to customers in Germany. The Federal Aviation Administration (FAA) made similar promises last year, but their efforts were stymied by growing concerns from local and national law-enforcement agencies.


Tech and the future of transportation: From here to there

ZDNet

Articles about technology and the future of transportation rarely used to get far without mentioning jet-packs: a staple of science fiction from the 1920s onwards, the jet pack became a reality in the 1960s in the shape of devices such as the Bell Rocket Belt. But despite many similar efforts, the skies over our cities remain stubbornly free of jet-pack-toting commuters.


Trump OKs test program to expand domestic drone flights

FOX News

President Donald Trump gave the go-ahead Wednesday, signing a directive intended to increase the number and complexity of drone flights. The presidential memo would allow exemptions from current safety rules so communities could move ahead with testing of drone operations. States, communities and tribes selected to participate would devise their own trial programs in partnership with government and industry drone users. The Federal Aviation Administration would review each program. The agency would grant waivers, if necessary, to rules that now restrict drone operations.


Researchers, regulators prepare for drones to fill US skies

FOX News

From crop dusting to package delivery, commercial drones are about to become a part of everyday life. "Just in the last 18 months, we've registered twice as many unmanned aircraft (as) we registered all aircraft from the previous 100 years," said Earl Lawrence, director of the Federal Aviation Administration's Unmanned Aircraft Systems Integration Office. To safely integrate the vast numbers of new unmanned aircraft systems (UAS) into the nation's airspace, the FAA is relying on a group of 23 research institutions led by Mississippi State University. The Alliance for System Safety of UAS through Research Excellence (ASSURE) is conducting in-depth studies on virtually every aspect of drone operations, including air traffic control, pilot certification and crash avoidance. "What happens when a drone hits a wing or a windshield or any other part of the aircraft is (one) of our key questions," Lawrence said.