The advent of drones is leading to a paradigm shift in courier services,while their large-scale deployment is confined by a limited range.Here,we design a low-cost product that allows drones to drop parcels onto and p...The advent of drones is leading to a paradigm shift in courier services,while their large-scale deployment is confined by a limited range.Here,we design a low-cost product that allows drones to drop parcels onto and pick them up from the roofs of moving passenger vehicles.With this,we propose a ground-air cooperation(GAC)based business model for parcel delivery in an urban environment.As per our case study using real-world data in Beijing,the new business model will not only shorten the parcel delivery time by 86.5% with a comparable cost,but also reduce road traffic by 8.6%,leading to an annual social benefit of 6.67 billion USD for Beijing.The proposed model utilizes the currently“wasted or unused”rooftops of passenger vehicles and has the potential to replace most parcel trucks and trailers,thus fundamentally addressing the congestion,noise,pollution,and road wear and tear problems caused by trucks,and bringing in immense social benefit.展开更多
Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devo...Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.展开更多
The advancement of Unmanned Aerial Vehicle(UAV) technology in terms of industrial processes and communication and networking technologies has led to an increase in their use in civil, business, and social applications...The advancement of Unmanned Aerial Vehicle(UAV) technology in terms of industrial processes and communication and networking technologies has led to an increase in their use in civil, business, and social applications. Global rules in most countries had previously limited the use of drones to military applications due to their deployment in the open air, drones are likely to be lost, destroyed, or physically hijacked. However, more recently, the presence of COVID-19 has forced the world to present new implementing measures which will also widen the use of drones in civil and commercial and social applications, especially now in the delivery of medicines for medical home care. In the period of required public isolation as a consequence of the SARS-COV-2 pandemic, this knowledge has become one of the principal partners in the fight against the coronavirus. This paper offers a summary of the medical drone manufacturing, with a specific emphasis on its approval by the pharmaceutical sector to solve logistical problems in healthcare during times of sensitive need. We also discuss the numerous challenges to be met in the integration of drones to save our lives and suggest future research directions. The question that arises for this problem, how to optimize delivery medical supplies times in-home health care made up of drones? We conducted a synthesis literature review devoted to the use of UAVs in healthcare with their different aspects. A total of different research made are given to describe the role of UAV in Home healthcare with the presence of SARS-COV-2. We conclude that the drones will be able to optimize the way of eliminating contamination with a very high percentage(through the reduction of human contact) with the increase of the flexibility of the flight(reaching the less accessible regions every hour of the day).展开更多
文摘The advent of drones is leading to a paradigm shift in courier services,while their large-scale deployment is confined by a limited range.Here,we design a low-cost product that allows drones to drop parcels onto and pick them up from the roofs of moving passenger vehicles.With this,we propose a ground-air cooperation(GAC)based business model for parcel delivery in an urban environment.As per our case study using real-world data in Beijing,the new business model will not only shorten the parcel delivery time by 86.5% with a comparable cost,but also reduce road traffic by 8.6%,leading to an annual social benefit of 6.67 billion USD for Beijing.The proposed model utilizes the currently“wasted or unused”rooftops of passenger vehicles and has the potential to replace most parcel trucks and trailers,thus fundamentally addressing the congestion,noise,pollution,and road wear and tear problems caused by trucks,and bringing in immense social benefit.
基金supported by the Key Research and Development Program of Shaanxi (2022GXLH-02-09)the Aeronautical Science Foundation of China (20200051053001)the Natural Science Basic Research Program of Shaanxi (2020JM-147)。
文摘Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.
文摘The advancement of Unmanned Aerial Vehicle(UAV) technology in terms of industrial processes and communication and networking technologies has led to an increase in their use in civil, business, and social applications. Global rules in most countries had previously limited the use of drones to military applications due to their deployment in the open air, drones are likely to be lost, destroyed, or physically hijacked. However, more recently, the presence of COVID-19 has forced the world to present new implementing measures which will also widen the use of drones in civil and commercial and social applications, especially now in the delivery of medicines for medical home care. In the period of required public isolation as a consequence of the SARS-COV-2 pandemic, this knowledge has become one of the principal partners in the fight against the coronavirus. This paper offers a summary of the medical drone manufacturing, with a specific emphasis on its approval by the pharmaceutical sector to solve logistical problems in healthcare during times of sensitive need. We also discuss the numerous challenges to be met in the integration of drones to save our lives and suggest future research directions. The question that arises for this problem, how to optimize delivery medical supplies times in-home health care made up of drones? We conducted a synthesis literature review devoted to the use of UAVs in healthcare with their different aspects. A total of different research made are given to describe the role of UAV in Home healthcare with the presence of SARS-COV-2. We conclude that the drones will be able to optimize the way of eliminating contamination with a very high percentage(through the reduction of human contact) with the increase of the flexibility of the flight(reaching the less accessible regions every hour of the day).
