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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... 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 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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... 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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A team of networked UAVs are deployed in an unknown re-gion to search and destroy targets. To successfully destroy a target, a coalition of UAVs with sufficient cumulative re-sources needs to be assigned. Forming coalitions under net-works with dynamic topology is difficult and the type of coalition formation strategy adopted affects the mission per-formance. In this paper, we determine a mechanism to form coalitions in dynamically changing networks and investigate different coalition formation strategies.
... 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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... The approaches based on the minimization of a cost function can take into account different and conflicting constraints [6,7] that range from the fuel consumption minimization to the information availability about the displacement (in terms of positions) of other vehicles joining to the fleet. ...
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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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As the community strives towards autonomous multi-robot systems, there is a need for these systems to autonomously form coalitions to complete assigned missions. Numerous coalition formation algorithms have been proposed in the software agent literature. Algorithms exist that form agent coalitions in both super additive and non-super additive environments. The algorithmic techniques vary from negotiation-based protocols in multi-agent system (MAS) environments to those based on computation in distributed problem solving (DPS) environments. Coalition formation behaviors have also been discussed in relation to game theory. Despite the plethora of MAS coalition formation literature, to the best of our knowledge none of the proposed algorithms have been demonstrated with an actual multi-robot system. There exists a discrepancy between the multi-agent algorithms and their applicability to the multi-robot domain. This paper aims to bridge that discrepancy by unearthing the issues that arise while attempting to tailor these algorithms to the multi-robot domain. A well-known multi-agent coalition formation algorithm has been studied in order to identify the necessary modifications to facilitate its application to the multi-robot domain. This paper reports multi-robot coalition formation results based upon simulation and actual robot experiments. A multi-agent coalition formation algorithm has been demonstrated on an actual robot system
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Coalition formation is a key topic in multiagent systems. One may prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one. Furthermore, finding the optimal coalition structure is NP-complete. But then, can the coalition structure found via a partial search be guaranteed to be within a bound from optimum? We show that none of the previous coalition structure generation algorithms can establish any bound because they search fewer nodes than a threshold that we show necessary for establishing a bound. We present an algorithm that establishes a tight bound within this minimal amount of search, and show that any other algorithm would have to search strictly more. The fraction of nodes needed to be searched approaches zero as the number of agents grows. If additional time remains, our anytime algorithm searches further, and establishes a progressively lower ...