Unmanned aerial vehicles applications in future smart cities

Mohamed, Nader; Al-Jaroodi, Jameela; Jawhar, Imad; Idries, Ahmed; Mohammed, Farhan

doi:10.1016/j.techfore.2018.05.004

Cited by 284 publications

(146 citation statements)

References 33 publications

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“…As shown in Fig. 3, the robot presented a low risk of collision, since the distance to the closest obstacle lies 4 Optitrack motion capture system https://optitrack.com/ in the range [1,3] m with median 1.7 m and the T T C −1 values are concentrated around −0.09s −1 with a minimum value of −0.4 s −1 . In addition, our approach presents fast real-time capabilities with a median control delay of 2.4 ms.…”

Section: One-horizon Benchmarkmentioning

confidence: 99%

“…Autonomous robots, such as self-driving cars or drones, are expected to revolutionize transportation, inspection and many other applications to come [1]. To fully exploit their capabilities, we need to enable their safe operation among humans and other robots while pursuing high-level objectives such as safety [2] or energy consumption [3].…”

Section: Introductionmentioning

confidence: 99%

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A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

Castillo-Lopez

Ludivig

Sajadi-Alamdari

et al. 2020

IEEE Robot. Autom. Lett.

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Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians.

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Section: One-horizon Benchmarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

Castillo-Lopez

Ludivig

Sajadi-Alamdari

et al. 2020

IEEE Robot. Autom. Lett.

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show abstract

“…The key stakeholders for addressing these challenges all have a distinct role. Governing bodies work on introducing regulations, policies and operations guidelines to ensure the safe use of UAVs; researchers design models and architectures to build, integrate and deploy UAV applications; and the industry aims to develop and introduce value-added features to smart cities using UAVs [47].…”

Section: Unmanned Aerial Vehicles and Personal Aerial Vehiclesmentioning

confidence: 99%

“…Infrastructure requirements, such as the number of landing and parking spaces, proper flying corridors, interaction with other modes and synergies with autonomous ground vehicles (PAVs and CAVs can benefit and complement each other), may allow PAVs to contribute significantly in the decrease of congestion and pollution if planned and implemented in a right way [49]. Nonetheless, despite innovation progress in the PAV concept development and related technologies, there are still key challenges that remain regarding public acceptance, traffic safety, expensive infrastructure, disaster management, trespassing and unnecessary surveillance, visual intrusion and excessive air traffic concerns [47,50].…”

Section: Unmanned Aerial Vehicles and Personal Aerial Vehiclesmentioning

confidence: 99%

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

et al. 2020

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Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential, if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built, and risks should be eliminated, for this transition to take off. This paper provides a novel conceptual contribution that thoroughly discusses the scarcely studied nexus of AI, transportation and the smart city and how this will affect urban futures. It specifically covers key smart mobility initiatives referring to Connected and Autonomous Vehicles (CAVs), autonomous Personal and Unmanned Aerial Vehicles (PAVs and UAVs) and Mobility-as-a-Service (MaaS), but also interventions that may work as enabling technologies for transport, such as the Internet of Things (IoT) and Physical Internet (PI) or reflect broader transformations like Industry 4.0. This work is ultimately a reference tool for researchers and city planners that provides clear and systematic definitions of the ambiguous smart mobility terms of tomorrow and describes their individual and collective roles underpinning the nexus in scope.

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“…Governance of emerging technology should be viewed from the historical context based on some flexibility in new developments [12]; coordinated between the regulator and the regulated [51]. Several aspects, like cooperation, collaboration, accountability and transparency, are essential for UAV technology governance [52]. Therefore, how to address the issues of governance for civilian UAV innovations in crop insurance applications.…”

Section: Governance Of Innovations For Sustainabilitymentioning

confidence: 99%

Securing sustainability in Indian agriculture through civilian UAV: a responsible innovation perspective

Chamuah

Singh

2019

SN Appl. Sci.

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Emerging technology like civilian unmanned aerial vehicle (UAV) has the potential to exert an impact on agriculture production and crop damages assessment. Securing sustainability in agriculture requires accountability and responsibility of the actors engaged in the deployment of the civilian UAV due to associated deployment risks and unintended consequences. UAV technology has the potential to replace remote sensing technologies like satellite imageries and piloted aircraft used in the crop insurance business. The governance of deployment of UAV technology in India is a complex challenge when a well-developed regulatory system is not in place and diverse actors involved in the deployment and operation of civilian UAV for agricultural applications. Therefore, two main questions, how UAV innovations can lead to sustainability in Indian agriculture and how are the issues of governance of civil UAV innovations in crop insurance applications addressed, are dealt with. The responsible innovation approach is adopted as a theoretical framework. The exploratory and qualitative study used in-depth interviews, and the interviewees were selected through snowball sampling technique. The results suggest that in the governance of emerging technologies like UAV certain values such as trust, transparency, safety, autonomy, and environmental friendliness assumed high significance. Findings also suggest that UAV has the risk-taking ability in adverse weather condition. The UAV technology also creates values (social, economic and environmental) for deployment in the crop insurance business in India.

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Unmanned aerial vehicles applications in future smart cities

Cited by 284 publications

References 33 publications

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

Securing sustainability in Indian agriculture through civilian UAV: a responsible innovation perspective

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