Conference Paper

# Multiple UAV coalition formation

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## Abstract

Unmanned aerial vehicles (UAVs) have the potential to carry munitions in support of battlefield operations, however they have limited sensor range and can carry only small quantities of resources. Often, to fully prosecute a target, a variety of assets may be required, and it may be necessary to deliver these assets simultaneously. Therefore, a team of UAVs that satisfies the target resource requirement needs to be assigned to a single target, and this team is called a coalition. Other desired requirements for the coalition are (i) minimize the target prosecution delay and (ii) minimize the size of the coalition. In this paper, we propose a two-stage optimal coalition formation algorithm that assigns appropriate numbers of UAVs satisfying the desired requirements. We developed a Dubins curves based simultaneous strike scheme. Simulation results are presented to show that the two-stage coalition formation algorithm has low computational overhead and can be applied in real-time.

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... One aspect of the task allocation problem is what is known as the coalition formation problem: How do we form teams of agents, with each team assigned to a particular task, in order to best complete the set of tasks at hand? While the coalition formation problem in multi-agent systems has received much atten- tion [4,12,14,16], only a small fraction of that work has focused on multi-robot domains [2,5,15,17,20]. The multi-agent algorithms and techniques are not directly transferable to multi-robot domains, as multi-robot domains present challenges and constraints not encountered with software agents [19]. ...
... Abdallah and Lesser [1] demonstrate that the multi-dimensional knapsack problem is reducible to the coalition formation problem. While significant research exists for multi-agent coalition formation [4,12,14,16], only a small fraction of research has focused on multi-robot domains [2,5,15,17,20]. Vig and Adams [19,20] demonstrate some of the differences between general multi-agent and multirobot domains and highlight the fact that the general multi-agent algorithms and techniques are not directly transferable to multi-robot domains, as multi-robot domains present challenges and constraints not encountered with software agents. Vig and Adams [20] introduce the service oriented model employed in this paper and present a market based approach, RACHNA that attempts to address some of the issues with coalition formation in multi-robot domains. ...
Article
This paper focuses on coalition formation for task allocation in both multi-agent and multi-robot domains. Two different problem formalizations are considered, one for multi-agent domains where agent resources are transferable and one for multi-robot domains. We demonstrate complexity theoretic differences between both models and show that, under both, the coalition formation problem, with m tasks, is NP-hard to both solve exactly and to approximate within a factor of $${O(m^{1-\epsilon})}$$ for all $${\epsilon > 0}$$. Two natural restrictions of the coalition formation problem are considered. In the first situation agents are drawn from a set of j types. Agents of each type are indistinguishable from one another. For this situation a dynamic programming based approach is presented, which, for fixed j finds the optimal coalition structure in polynomial time and is applicable in both multi-agent and multi-robot domains. We then consider situations where coalitions are restricted to k or fewer agents. We present two different algorithms. Each guarantees the generated solution to be within a constant factor, for fixed k, of the optimal in terms of utility. Our algorithms complement Shehory and Kraus’ algorithm (Artif Intell 101(1–2):165–200, 1998), which provides guarantee’s on solution cost, as ours provides guarantees on utility. Our algorithm for general multi-agent domains is a modification of and has the same running time as Shehory and Kraus’ algorithm, while our approach for multi-robot domains runs in time $${O(n^{\frac{3}{2}}m)}$$, much faster than Vig and Adams (J Intell Robot Syst 50(1):85–118, 2007) modifications to Shehory and Kraus’ algorithm for multi-robot domains, which ran in time O(n k m), for n agents and m tasks.
... In this section, an algorithm for MPC formation flight of two quadrotor is presented. Formation Control has been addressed in [11][12][13][14][15][16][17][18][19][20]. However, in the near future it will be possible to develop control techniques using formation control not only for UAVs but also for UGVs. ...
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... Experimental demonstrations of the distributed auction-based approach for the box pushing problem [18,44], cooperative load transportation [27], and disaster management [24] were respectively presented. The coalition formation for the simultaneous attack problem was treated using a centralized [42] and distributed scheme [25,41]. The centralized scheme solved the combinatorial optimization problem for the coalition formation using the particle swarm optimization (PSO) technique, and the distributed scheme utilized an auction and an integer programming. ...
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New market-based decentralized algorithms are proposed for the task assignment of multiple unmanned aerial vehicles in dynamic environments with a limited communication range. In particular, a cooperative timing mission that cannot be performed by a single vehicle is considered. The baseline algorithms for a connected network are extended to deal with time-varying network topology including isolated subnetworks due to a limited communication range. The mathematical convergence and scalability analyses show that the proposed algorithms have a polynomial time complexity, and numerical simulation results support the scalability of the proposed algorithm in terms of the runtime and communication burden. The performance of the proposed algorithms is demonstrated via Monte Carlo simulations for the scenario of the suppression of enemy air defenses.
... The goal of flock formation, which is directly related to the research area of coalition formation [38][39][40][41], is to form groups of heterogeneous entities with shared or individual goals. Several studies on coalition formation have considered mobile entities, such as UAVs, vehicles, and/or mobile robots [42][43][44][45]. Most of the studies on coalition formation focus on task sharing and resource allocation challenges in the coalition. ...
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In recent years, unmanned aerial vehicles (UAVs) have been used in a wide range of domains. As the number of UAV flights increases, central flight areas may become overcrowded. This may cause delays in UAV traffic or gridlock on UAV routes. To address this challenge, we propose a flocking protocol that enables individual UAVs to optimize their flight preferences, while receiving the benefits of traveling in a group. Flocking can reduce overcrowding , and as a result, UAVs will be able to travel on less congested routes and have fewer encounters with other UAVs, thus reducing flight time and conserving energy. The protocol allows each UAV to create a flock or join an existing flock for all or part of its journey. We perform a simulation of UAV flights in an urban area and compare the average flight time of the UAVs based on various flight situations (e.g., different routes and participation in groups of UAVs). The simulation results demonstrate that the use of the flocking protocol significantly reduced the average flight time, and the flight saving rate increased with an increase in the UAV mean number. A similar effect is also observed in situations where each UAV and flock formed a dynamic A* based path in advance to avoid collisions.
... In another attempt, the Fast Marching Square (FM2) algorithm [14]- [17] has been verified for obtaining excellent results on mobile vehicles, but kinematic constraints have not been considered to validate the feasibility of the trajectories. The Dubins path was introduced as a suitable method to solve this problem in 2D [18], [19] and 3D [20] environments. However, the resulting paths are not optimized. ...
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This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (UAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of UAVs while simultaneously avoid obstacles, and maintain altitude constraints as well as the shape of the UAV formation. A multiple-objective optimisation algorithm, called the Angle-encoded Particle Swarm Optimization (theta-PSO) algorithm, is proposed to accelerate the swarm convergence with angular velocity and position being used for the location of particles. The whole formation is modelled as a virtual rigid body and controlled to maintain a desired geometric shape among the paths created while the centroid of the group follows a pre-determined trajectory. Based on the testbed of 3DR Solo drones equipped with a proprietary Mission Planner, and the Internet-of-Things (IoT) for multi-directional transmission and reception of data between the UAVs, extensive experiments have been conducted for triangular formation maintenance along a monorail bridge. The results obtained confirm the feasibility and effectiveness of the proposed approach.
... In another attempt, the Fast Marching Square (FM2) algorithm [14]- [17] has been verified for obtaining excellent results on mobile vehicles, but kinematic constraints have not been considered to validate the feasibility of the trajectories. The Dubins path was introduced as a suitable method to solve this problem in 2D [18], [19] and 3D [20] environments. However, the resulting paths are not optimized. ...
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... There are also some scholars who study the agent team formation problem in various application fields, such as RoboCup [33], rescue problem in fire field [27,34], unmanned aerial vehicles coalition formation strategies [35], agent team formation in social networks [36][37][38], business projects [39], engineering projects [40], and design problem [41]. In each specific field, there are some particular goal and constraints needing to be considered. ...
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... Once the coalition is determined using any of the strategies, the CL sends accept and reject decisions with ETAT at the target to the accepted PCM. The accept members assume the role of coalition members and have to prosecute the target simultaneously (which is one of the mission requirements) [7]. ...
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... In this section, an algorithm for MPC formation flight of two quadrotor is presented. Formation Control has been addressed in [57], [58], [59], [60], [61], [62], [63], [64], [65], [66] . However, in the near future it will be possible to develop control techniques using formation control not only for UAVs but also for UGVs. ...
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... Heimfarth et al. [7] have dealt with UAVs for Wireless Sensor Networks (WSNs) that are vulnerable to failures that may lead to the disconnection of parts of the network, creating partitions in the network, isolating the sensor nodes. This problem compromises the final results achieved by the WSN operation, as those isolated nodes are not able to deliver their messages to the sink nodes [8] . A way to overcome such problem is to provide an alternative connection to support the connectivity via other types of nodes that repair the connectivity among the sensor nodes. ...
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In this paper, based on task sequence and time constraint in the SEAD mission of multi-UAV, a heterogeneous multi-UAV cooperative task assignment mathematical model is established. We put forward a hybrid algorithm GSA-GA(gravity search algorithm-genetic algorithm) to resolve cooperative task assignment. The algorithm combines gravity search algorithm and genetic algorithm, improves the coding and decoding methods in updating the position. The simulation result shows that the GSA-GA has rapid convergence rate in solving the cooperative task assignment compared with the classic DPSO algorithm, and has the better resolution. © 2018, Editorial Board of Journal of Northwestern Polytechnical University. All right reserved.
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