Real-Time Task Allocation of Heterogeneous Unmanned Aerial Vehicles for Search and Prosecute Mission

In recent years, the Internet of Things (IoT) has developed rapidly after the era of computers and smart phones, which is expected to be applied to cities to improve the quality of life and realize the intelligence of smart cities. In particular, with the outbreak of coronavirus disease 2019 (COVID-19) last year, in order to reduce contact, some IoT devices, such as robots, unmanned aerial vehicles (UAVs), and unmanned vehicles, have played a great role in temperature monitoring, goods delivery, and so on. In this paper, we study the real-time task allocation problem of heterogeneous UAVs searching and delivering goods in the city. Considering the resource requirement of task and resource constraints of the UAV, when the resource of a single UAV cannot meet the requirement of the task, we propose a method of forming a UAV coalition based on contract net protocol. We analyze the coalition formation problem from two aspects: mission completion time and UAV’s energy consumption. Firstly, the mathematical model is established according to the optimization objective and condition constraints. Then, according to the established mathematical model, different coalition formation algorithms are proposed. To minimize the mission completion time, we propose a two-stage coalition formation algorithm. Aiming at minimizing the UAV’s energy consumption, it is transformed into a zero-one integer programming problem, which can be solved by the existing solver. Then, considering both mission completion time and energy consumption, we propose a coalition formation algorithm based on a resource tree. Finally, we design some simulation experiments and compare with the task allocation algorithm based on resource welfare. The simulation results show that our proposed algorithms are feasible and effective.

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A mutual-selecting market-based mechanism for dynamic coalition formation

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418755840 ◽

2018 ◽

Vol 15 (1) ◽

pp. 172988141875584 ◽

Cited By ~ 3

Author(s):

Bing Xie ◽

Shaofei Chen ◽

Jing Chen ◽

LinCheng Shen

Keyword(s):

Real Time ◽

Coalition Formation ◽

Task Allocation ◽

Completion Rate ◽

Utilization Rate ◽

Task Completion ◽

Distributed Agents ◽

Time Task ◽

Comparable Performance ◽

Negotiation Mechanism

This article presents a novel market-based mechanism for a dynamic coalition formation problem backgrounded under real-time task allocation. Specifically, we first analyze the main factors of the real-time task allocation problem, and formulate the problem based on the coalition game theory. Then, we employ a social network for communication among distributed agents in this problem, and propose a negotiation mechanism for agents forming coalitions on timely emerging tasks. In this mechanism, we utilize an auction algorithm for real-time agent assignment on coalitions, and then design a mutual-selecting method to acquire better performance on agent utilization rate and task completion rate. And finally, our experimental results demonstrate that our market-based mechanism has a comparable performance in task completion rate to a decentralized approach (within 25% better on average) and a centralized dynamic coalition formation method (within 10% less on average performance).

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Coalition formation among unmanned aerial vehicles for uncertain task allocation

Wireless Networks ◽

10.1007/s11276-017-1560-8 ◽

2017 ◽

Vol 25 (1) ◽

pp. 367-377 ◽

Cited By ~ 2

Author(s):

Xiaomei Fu ◽

Jing Zhang ◽

Liang Zhang ◽

Shuai Chang

Keyword(s):

Unmanned Aerial Vehicles ◽

Coalition Formation ◽

Task Allocation ◽

Aerial Vehicles

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A Possible Smart Metering System Evolution for Rural and Remote Areas Employing Unmanned Aerial Vehicles and Internet of Things in Smart Grids

Sensors ◽

10.3390/s21051627 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1627

Author(s):

Giovanni Battista Gaggero ◽

Mario Marchese ◽

Aya Moheddine ◽

Fabio Patrone

Keyword(s):

Energy Consumption ◽

Internet Of Things ◽

Unmanned Aerial Vehicles ◽

Smart Metering ◽

Rural And Remote ◽

Electrical Grid ◽

Remote Areas ◽

Aerial Vehicles ◽

Consumption Data ◽

Rural And Remote Areas

The way of generating and distributing energy throughout the electrical grid to all users is evolving. The concept of Smart Grid (SG) took place to enhance the management of the electrical grid infrastructure and its functionalities from the traditional system to an improved one. To measure the energy consumption of the users is one of these functionalities that, in some countries, has already evolved from a periodical manual consumption reading to a more frequent and automatic one, leading to the concept of Smart Metering (SM). Technology improvement could be applied to the SM systems to allow, on one hand, a more efficient way to collect the energy consumption data of each user, and, on the other hand, a better distribution of the available energy through the infrastructure. Widespread communication solutions based on existing telecommunication infrastructures instead of using ad-hoc ones can be exploited for this purpose. In this paper, we recall the basic elements and the evolution of the SM network architecture focusing on how it could further improve in the near future. We report the main technologies and protocols which can be exploited for the data exchange throughout the infrastructure and the pros and cons of each solution. Finally, we propose an innovative solution as a possible evolution of the SM system. This solution is based on a set of Internet of Things (IoT) communication technologies called Low Power Wide Area Network (LPWAN) which could be employed to improve the performance of the currently used technologies and provide additional functionalities. We also propose the employment of Unmanned Aerial Vehicles (UAVs) to periodically collect energy consumption data, with evident advantages especially if employed in rural and remote areas. We show some preliminary performance results which allow assessing the feasibility of the proposed approach.

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Adaptive Human-Robot Interactions for Multiple Unmanned Aerial Vehicles

Robotics ◽

10.3390/robotics10010012 ◽

2021 ◽

Vol 10 (1) ◽

pp. 12

Author(s):

Yixiang Lim ◽

Nichakorn Pongsarkornsathien ◽

Alessandro Gardi ◽

Roberto Sabatini ◽

Trevor Kistan ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Real Time ◽

Simulation Environment ◽

Adaptive Automation ◽

Cognitive States ◽

Aerial Vehicles ◽

Machine Interface ◽

Multiple Unmanned Aerial Vehicles ◽

Online Adaptation ◽

Adaptation Phase

Advances in unmanned aircraft systems (UAS) have paved the way for progressively higher levels of intelligence and autonomy, supporting new modes of operation, such as the one-to-many (OTM) concept, where a single human operator is responsible for monitoring and coordinating the tasks of multiple unmanned aerial vehicles (UAVs). This paper presents the development and evaluation of cognitive human-machine interfaces and interactions (CHMI2) supporting adaptive automation in OTM applications. A CHMI2 system comprises a network of neurophysiological sensors and machine-learning based models for inferring user cognitive states, as well as the adaptation engine containing a set of transition logics for control/display functions and discrete autonomy levels. Models of the user’s cognitive states are trained on past performance and neurophysiological data during an offline calibration phase, and subsequently used in the online adaptation phase for real-time inference of these cognitive states. To investigate adaptive automation in OTM applications, a scenario involving bushfire detection was developed where a single human operator is responsible for tasking multiple UAV platforms to search for and localize bushfires over a wide area. We present the architecture and design of the UAS simulation environment that was developed, together with various human-machine interface (HMI) formats and functions, to evaluate the CHMI2 system’s feasibility through human-in-the-loop (HITL) experiments. The CHMI2 module was subsequently integrated into the simulation environment, providing the sensing, inference, and adaptation capabilities needed to realise adaptive automation. HITL experiments were performed to verify the CHMI2 module’s functionalities in the offline calibration and online adaptation phases. In particular, results from the online adaptation phase showed that the system was able to support real-time inference and human-machine interface and interaction (HMI2) adaptation. However, the accuracy of the inferred workload was variable across the different participants (with a root mean squared error (RMSE) ranging from 0.2 to 0.6), partly due to the reduced number of neurophysiological features available as real-time inputs and also due to limited training stages in the offline calibration phase. To improve the performance of the system, future work will investigate the use of alternative machine learning techniques, additional neurophysiological input features, and a more extensive training stage.

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