In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum number of people. A novel technique for the SAR problem is proposed and referred to as the layered search and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR results shows that the percentages of rescued survivors clustered around the [78%-100%] range under an exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm focuses on the center of the disaster, it finds more survivors and rescues them faster than the other algorithms, with an average of 55%~77%. Moreover, most registered times to rescue survivors by LSAR are bounded by a time of 04:50:02 with 95% confidence for a one-month mission time.

https://www.cister.isep.ipp.pt/docs/lsar__multi_uav_collaboration_for_search_and_rescue_missions/1507/view.pdf

LSAR: Multi-UAV Collaboration for Search and Rescue Missions

In some embodiments, unmanned aerial task systems are provided that comprise multiple unmanned aerial vehicles (UAV) each comprising: a UAV control circuit; a motor; and a propulsion system coupled with the motor and configured to enable the respective UAVs to move themselves; and wherein a first UAV control circuit of a first UAV of the multiple UAVs is configured to access power level data corresponding to each of the multiple UAVs, and select a second UAV of the multiple UAVs based at least in part on a power level of the second UAV relative to a threshold power level corresponding to a first task to be performed and a predicted power usage by the second UAV while utilizing a first tool system temporarily cooperated with the second UAV in performing the first task.

Geographic area monitoring systems and methods that balance power usage between multiple unmanned vehicles

In some embodiments, unmanned aerial task systems are provided that comprise multiple unmanned aerial vehicles (UAV) each comprising: a UAV control circuit; a motor; and a propulsion system coupled with the motor and configured to enable the respective UAVs to move themselves; and wherein a first UAV control circuit of a first UAV of the multiple UAVs is configured to identify a second UAV carrying a first tool system configured to perform a first function, cause a notification to be communicated to the second UAV directing the second UAV to transfer the first tool system to the first UAV, and direct a first propulsion system of the first UAV to couple with the first tool system being transferred from the second UAV.

Geographic area monitoring systems and methods through interchanging tool systems between unmanned vehicles

The invention belongs to the field of unmanned aerial vehicle formation, and specifically to a communication relay based unmanned aerial vehicle formation cooperative target distribution method. The communication relay based unmanned aerial vehicle formation cooperative target distribution method comprises the steps of initializing a task chain of each unmanned aerial vehicle; 2, performing preliminary distribution on each unmanned aerial vehicle by utilizing a consistence negotiation auction mechanism based method according to a to-be-distributed target point set; 3, deleting communication relay points of undistributed unmanned aerial vehicles and subsequent to-be-scouted target points from the to-be-distributed target point set; 4, judging the communication connection condition of the unmanned aerial vehicles and a base during the time when the unmanned aerial vehicles perform scouting tasks at the to-be-performed target points, performing a step 6 if the communication connection isall right, and otherwise, performing a step 5; 5, generating communication relay points and adding the generated communication relay points into the to-be-distributed target point set and performing the step 2; and 6, outputting the task chain of each unmanned aerial vehicle if the task chains of all the unmanned aerial vehicles are not updated, and otherwise, performing the step 2. According to the communication relay based unmanned aerial vehicle formation cooperative target distribution method, multi-target scouting tasks can be finished at the minimum cost under the condition of satisfyinga communication radius constraint condition of the unmanned aerial vehicles, and the coverage of formation scouting of multiple targets is increased.

Communication relay based unmanned aerial vehicle formation cooperative target distribution method

In some embodiments, unmanned aerial task systems are provided that comprise: multiple unmanned aerial vehicles (UAV) each comprising: a UAV control circuit; a motor; and a propulsion system; and wherein data acquired through a first set of at least one of the multiple UAVs while performing a first set of at least one task is caused to be distributed to a second set of at least two of the multiple UAVs, and cause cooperative computational processing of the data through the UAV control circuits of the second set of UAVs and cooperatively identify based on the cooperative computational processing a second set of at least one task to be performed, and identify a set of at least two tool systems to be utilized by a third set of at least two of the multiple UAVs in cooperatively performing the second set of at least one task.

Geographic area monitoring systems and methods utilizing computational sharing across multiple unmanned vehicles

A mechanism for dynamically allocating tasks among multiple UAVs operating autonomously during a mission is discussed. Task assignment is adjusted by each UAV dynamically during the mission based on criteria related to the individual UAV's operational status and/or mission parameters. Task allocation is determined independently without group communication between the UAVs actively taking part in the mission and without direct communication to a ground-based controller. A communication UAV provides a shared memory space that may be utilized by each UAV in determining its own task allocation.

Dynamic task allocation in an autonomous multi-uav mission

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Dynamic task allocation in an autonomous multi-uav mission