scholarly journals Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions

2020 ◽  
Vol 13 (1) ◽  
pp. 27
Author(s):  
Amjaad Alhaqbani ◽  
Heba Kurdi ◽  
Kamal Youcef-Toumi
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Task Allocation ◽  
Optimal Allocation ◽  
Search And Rescue ◽  
Fleet Size ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Hostile Environments ◽  
Rescue Time ◽  
Multi Uav

The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.

Research on dynamic task allocation for multiple unmanned aerial vehicles

2017 ◽  
Vol 39 (4) ◽  
pp. 466-474 ◽  
Author(s):  
Yongfei Miao ◽  
Luo Zhong ◽  
Yufu Yin ◽  
Chengming Zou ◽  
Zhenjun Luo
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Task Allocation ◽  
Clonal Selection ◽  
Immune Memory ◽  
Allocation Model ◽  
Dynamic Task ◽  
Aerial Vehicles ◽  
Dynamic Task Allocation ◽  
Multi Agent ◽  
Multiple Unmanned Aerial Vehicles

To solve the distributed task allocation problems of search and rescue missions for multiple unmanned aerial vehicles (UAVs), this paper establishes a dynamic task allocation model under three conditions: 1) when new targets are detected, 2) when UAVs break down and 3) when unexpected threats suddenly occur. A distributed immune multi-agent algorithm (DIMAA) based on an immune multi-agent network framework is then proposed. The technologies employed by the proposed algorithm include a multi-agent system (MAS) with immune memory, neighbourhood clonal selection, neighbourhood suppression, neighbourhood crossover and self-learning operators. The DIMAA algorithm simplifies the decision-making process among agents. The simulation results show that this algorithm not only obtains the global optimum solution, but also reduces the communication load between agents.

scholarly journals Honeycomb Map: A Bioinspired Topological Map for Indoor Search and Rescue Unmanned Aerial Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 907 ◽  
Author(s):  
Ricardo da Rosa ◽  
Marco Aurelio Wehrmeister ◽  
Thadeu Brito ◽  
José Luís Lima ◽  
Ana Isabel Pinheiro Nunes Pereira
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Prior Knowledge ◽  
Energy Use ◽  
Search And Rescue ◽  
Data Types ◽  
Topological Map ◽  
Exploration Time ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Time And Energy

The use of robots to map disaster-stricken environments can prevent rescuers from being harmed when exploring an unknown space. In addition, mapping a multi-robot environment can help these teams plan their actions with prior knowledge. The present work proposes the use of multiple unmanned aerial vehicles (UAVs) in the construction of a topological map inspired by the way that bees build their hives. A UAV can map a honeycomb only if it is adjacent to a known one. Different metrics to choose the honeycomb to be explored were applied. At the same time, as UAVs scan honeycomb adjacencies, RGB-D and thermal sensors capture other data types, and then generate a 3D view of the space and images of spaces where there may be fire spots, respectively. Simulations in different environments showed that the choice of metric and variation in the number of UAVs influence the number of performed displacements in the environment, consequently affecting exploration time and energy use.

scholarly journals Task Assignment for Multi-UAV under Severe Uncertainty by Using Stochastic Multicriteria Acceptability Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoxuan Hu ◽  
Jing Cheng ◽  
He Luo
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Task Assignment ◽  
Uncertain Information ◽  
Simulation Experiments ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Severe Uncertainty ◽  
Task Assignment Problem ◽  
Multi Uav ◽  
Assignment Approach

This paper considers a task assignment problem for multiple unmanned aerial vehicles (UAVs). The UAVs are set to perform attack tasks on a collection of ground targets in a severe uncertain environment. The UAVs have different attack capabilities and are located at different positions. Each UAV should be assigned an attack task before the mission starts. Due to uncertain information, many criteria values essential to task assignment were random or fuzzy, and the weights of criteria were not precisely known. In this study, a novel task assignment approach based on stochastic Multicriteria acceptability analysis (SMAA) method was proposed to address this problem. The uncertainties in the criteria were analyzed, and a task assignment procedure was designed. The results of simulation experiments show that the proposed approach is useful for finding a satisfactory assignment under severe uncertain circumstances.

scholarly journals Autonomous Integrity Monitoring for Relative Navigation of Multiple Unmanned Aerial Vehicles

2021 ◽  
Vol 13 (8) ◽  
pp. 1483
Author(s):  
Yuan Sun
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Data ◽  
Wide Band ◽  
Relative Navigation ◽  
Monitoring Method ◽  
Integrity Monitoring ◽  
Position Information ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Multi Uav

Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely used due to its worldwide coverage and simplicity in relative navigation. However, the observations of GNSS are vulnerable to different kinds of faults arising from transmission degradation, ionospheric scintillations, multipath, spoofing, and many other factors. In an effort to improve the reliability of multi-UAV relative navigation, an autonomous integrity monitoring method is proposed with a fusion of double differenced GNSS pseudoranges and Ultra Wide Band (UWB) ranging units. Specifically, the proposed method is designed to detect and exclude the fault observations effectively through a consistency check algorithm in the relative positioning system of the UAVs. Additionally, the protection level for multi-UAV relative navigation is estimated to evaluate whether the performance meets the formation flight and collision avoidance requirements. Simulated experiments derived from the real data are designed to verify the effectiveness of the proposed method in autonomous integrity monitoring for multi-UAV relative navigation.

Sign in / Sign up

Export Citation Format

Share Document