Predictive Receding-Horizon Multi-Robot Task Allocation with Moving Tasks*

This paper addresses a multi-robot task allocation (MRTA) towards moving tasks and presents a novel computationally efficient predictive allocation algorithm that requires solving a linear program (LP) problem. Following the receding horizon control policy, the present algorithm repeats the optimization of future task assignments within an allocation horizon while predicting the evolution of the system. The online optimization is formulated so that the assignment problem is reduced exactly to an LP. The algorithm is also compared with other traditional methods, namely, the greedy approach and a genetic algorithm (GA). Our results show that the algorithm herein proposed outperforms the greedy approach for small prediction horizons and has significantly lower computational load than GA

Crowdsourcing complex workflows under budget constraints

We consider the problem of task allocation in crowdsourcing systems with multiple complex workflows, each of which consists of a set of interdependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a budget constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then determines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the corresponding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper

An approach to the multi-axis problem in manual control

The multiaxis control problem is addressed within the context of the optimal pilot model. The problem is developed to provide efficient adaptation of the optimal pilot model to complex aircraft systems and real world, multiaxis tasks. This is accomplished by establishing separability of the longitudinal and lateral control problems subject to the constraints of multiaxis attention and control allocation. Control solution adaptation to the constrained single axis attention allocations is provided by an optimal control frequency response algorithm. An algorithm is developed to solve the multiaxis control problem. The algorithm is then applied to an attitude hold task for a bare airframe fighter aircraft case with interesting multiaxis properties

Delay aware optimal resource allocation in MU MIMO-OFDM using enhanced spider monkey optimization

In multiple users MIMO- OFDM system allocates the available resources to the optimal users is a difficult task. Hence the scheduling and resource allocation become the major problem in the wireless network mainly in case of multiple input and multiple output method that has to be made efficient. There is various method introduced to give an optimal solution to the problem yet it has many drawbacks. So we propose this paper to provide an efficient solution for resource allocation in terms of delay and also added some more features such as high throughout, energy efficient and fairness. To make optimal resource allocation we introduce optimization algorithm named spider monkey with an enhancement which provides the efficient solution. In this optimization process includes the scheduling and resource allocation, the SNR values, channel state information (CSI) from the base station. To make more efficient finally we perform enhanced spider - monkey algorithm hence the resource allocation is performed based on QoS requirements. Thus the simulation results in our paper show high efficiency when compared with other schedulers and techniques

Efficient decentralized task allocation for UAV swarms in multi-target surveillance missions

This paper deals with the large-scale task allocation problem for Unmanned Aerial Vehicle (UAV) swarms in surveillance missions. The task allocation problem is proven to be NP-hard which means that finding the optimal solution requires exponential time. This paper presents a practically efficient decentralized task allocation algorithm for UAV swarms based on lazy sample greedy. The proposed algorithm can provide a solution with an expected optimality ratio of at least p for monotone submodular objective functions and of p(1−p) for non-monotone submodular objective functions. The individual computational complexity for each UAV is O(pr2), where p∈(0,0.5] is the sampling probability, r is the number of tasks. The performance of the proposed algorithm is testified through digital simulations of a multi-target surveillance mission. Simulation results indicate that the proposed algorithm achieves a comparable solution quality to state-of-the-art algorithms with dramatically less running time. Moreover, a trade-off between the solution quality and the running time is obtained by adjusting the sampling probabilit

Threshold bundle-based task allocation for multiple aerial robots

This paper focuses on the large-scale task allocation problem for multiple Unmanned Aerial Vehicles (UAVs). One of the great challenges with task allocation is the NP-hardness for both computation and communication. This paper proposes an efficient decentralised task allocation algorithm for multiple UAVs to handle the NP-hardness while providing an optimality bound of solution quality. The proposed algorithm can reduce computational and communicating complexity by introducing a decreasing threshold and building task bundles based on the sequential greedy algorithm. The performance of the proposed algorithm is examined through Monte-Carlo simulations of a multi-target surveillance mission. Simulation results demonstrate that the proposed algorithm achieves similar solution quality compared with benchmark task allocation algorithms but consumes much less running time and consensus steps

Joint Computing Offloading and Resource Allocation for Classification Intelligent Tasks in MEC Systems

Mobile edge computing (MEC) enables low-latency and high-bandwidth applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for intelligent tasks in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum system performance. We focus on classification intelligence tasks and formulate an optimization problem that considers both the accuracy requirements of tasks and the parallel computing capabilities of MEC systems. To solve the optimization problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation, and compression offloading. We use convex optimization and successive convex approximation to derive closed-form expressions for the subcarrier allocation, offloading decisions, computing capacity, and compressed ratio. Based on our solutions, we design an efficient computing offloading and resource allocation algorithm for intelligent tasks in MEC systems. Our simulation results demonstrate that our proposed algorithm significantly improves the performance of intelligent tasks in MEC systems and achieves a flexible trade-off between system revenue and cost considering intelligent tasks compared with the benchmarks.Comment: arXiv admin note: substantial text overlap with arXiv:2307.0274