This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.

The ultraviolet A (UVA) (315-400 nm) daily exposures and maximum daily irradiances from the Global Ozone Monitoring Experiment (GOME)-2 satellite have been compared over three years to the corresponding data from a groundbased spectroradiometer for a subtropical Southern Hemisphere site. This is one of the first such comparisons for the GOME-2 UVA waveband in the Southern Hemisphere. For the UVA daily exposures and the maximum daily irradiances, the comparisons were undertaken for all sky conditions and for cloud-free conditions. Under cloud-free conditions the R2of the fit regression line for the comparisons was 0.93 for the exposures and the irradiances. The influence of cloud reduced the R2values to 0.86 and 0.70 for the daily exposures and maximum irradiances, respectively. The relative root-mean-square error (rRMSE), mean absolute bias error (MABE), and mean bias error (MBE) for the maximum daily UVA irradiances on the cloud-free days were 0.08, 6.59 ± 7.32%, and -1.04 ± 9.83%, respectively. Similarly, for the daily UVA exposures on the cloud-free days, the rRMSE, MABE, and MBE were 0.10, 5.19 ± 6.42%, and -0.79 ± 8.24%, respectively. For the all-sky conditions, the corresponding values were 0.20, 15.23 ± 14.90%, and -0.79 ± 8.24% for the maximum daily irradiances and 0.19, 14.17 ± 14.56%, and -4.63 ± 20.60% for the daily exposures. In all studies of the influence of UVA on human health, this can complement ground-based measurements that provide the higher temporal and spatial resolution available only at a limited number of surface monitoring sites.

Rescue missions require both speed to meet strict time constraints and maximum use of resources. This study presents a Task Swap Allocation (TSA) algorithm that increases vehicle allocation with respect to the state-of-the-art consensus-based bundle algorithm and one of its extensions, while meeting time constraints. The novel idea is to enable an online reconfiguration of task allocation among distributed and networked vehicles. The proposed strategy reallocates tasks among vehicles to create feasible spaces for unallocated tasks, thereby optimizing the total number of allocated tasks. The algorithm is shown to be efficient with respect to previous methods because changes are made to a task list only once a suitable space in a schedule has been identified. Furthermore, the proposed TSA can be employed as an extension for other distributed task allocation algorithms with similar constraints to improve performance by escaping local optima and by reacting to dynamic environments.