A heuristic distributed task allocation method for multivehicle multitask problems and its application to search and rescue scenario

Using distributed task allocation methods for cooperating multivehicle systems is becoming increasingly attractive. However, most effort is placed on various specific experimental work and little has been done to systematically analyze the problem of interest and the existing methods. In this paper, a general scenario description and a system configuration are first presented according to search and rescue scenario. The objective of the problem is then analyzed together with its mathematical formulation extracted from the scenario. Considering the requirement of distributed computing, this paper then proposes a novel heuristic distributed task allocation method for multivehicle multitask assignment problems. The proposed method is simple and effective. It directly aims at optimizing the mathematical objective defined for the problem. A new concept of significance is defined for every task and is measured by the contribution to the local cost generated by a vehicle, which underlies the key idea of the algorithm. The whole algorithm iterates between a task inclusion phase, and a consensus and task removal phase, running concurrently on all the vehicles where local communication exists between them. The former phase is used to include tasks into a vehicle’s task list for optimizing the overall objective, while the latter is to reach consensus on the significance value of tasks for each vehicle and to remove the tasks that have been assigned to other vehicles. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution and can achieve outstanding performance in comparison with the consensus-based bundle algorithm

Niche shifts and the potential distribution of <i>Phenacoccus solenopsis</i> (Hemiptera: Pseudococcidae) under climate change

<div><p>The cotton mealybug, <i>Phenacoccus solenopsis</i> Tinsley (Hemiptera: Pseudococcidae), is a serious invasive species that significantly damages plants of approximately 60 families around the world. It is originally from North America and has also been introduced to other continents. Our goals were to create a current and future potential global distribution map for this pest under climate change with MaxEnt software. We tested the hypothesis of niche conservatism for <i>P</i>. <i>solenopsis</i> by comparing its native niche in North America to its invasive niches on other continents using Principal components analyses (PCA) in R. The potentially suitable habitat for <i>P</i>. <i>solenopsis</i> in its native and non-native ranges is presented in the present paper. The results suggested that the mean temperature of the wettest quarter and the mean temperature of the driest quarter are the most important environmental variables determining the potential distribution of <i>P</i>. <i>solenopsis</i>. We found strong evidence for niche shifts in the realized climatic niche of this pest in South America and Australia due to niche unfilling; however, a niche shift in the realized climatic niche of this pest in Eurasian owing to niche expansion.</p></div

Future species distribution models under climate change scenarios RCP 2.6–2050.

<p>Gray = unsuitable habitat area; Blue = low habitat suitability area; Green = moderate habitat suirability area; Red = highly habitat suitability area. The base map was created with Natural Earth Dataset (<a href="http://www.naturalearthdata.com/" target="_blank">http://www.naturalearthdata.com/</a>).</p

Future species distribution models under climate change scenarios RCP 8.5–2050.

<p>Gray = unsuitable habitat area; Blue = low habitat suitability area; Green = moderate habitat suirability area; Red = highly habitat suitability area. The base map was created with Natural Earth Dataset (<a href="http://www.naturalearthdata.com/" target="_blank">http://www.naturalearthdata.com/</a>).</p

Area with suitability under different climate scenarios.

<p>Area with suitability under different climate scenarios.</p

Native and invasive niches of <i>P</i>. <i>solenopsis</i> in different regions; multivariate climatic space was calculated using PCA-env method.

<p>PC1 and PC2 represent the first two axes of the principal competent analysis (PCA). The green and red shadings represent density of species occurrences in different regions; blue represents overlap. Solid and dashed lines show 100% and 50% of the available (background) environment. The red arrows show how the center of the climatic niche for <i>P</i>. <i>solenopsis</i> (solid) and background extent (dotted) has moved between two ranges.</p

Response curves showing the relationships between the probability of presence of P. solenopsis and seven bioclimatic variables.

<p>Values shown are average over 10 replicate runs: blue margins show ±SD calculated over 10 replicates.</p

Comparison of future potential suitable habitat areas for <i>P</i>. <i>solenopsi</i> by MaxEnt for time frames on different climate scenarios.

<p>Blue = low habitat suitability area; Green = moderate habitat suirability area; Red = highly habitat suitability area.</p

Native and invasive localities of <i>P</i>. <i>solenopsis</i> used in current modeling.

<p>Red dot represent native localities and blue dot represent invasive localities. The base map was created with Natural Earth Dataset (<a href="http://www.naturalearthdata.com/" target="_blank">http://www.naturalearthdata.com/</a>).</p

Relative contribution of each environmental variables to MaxEnt model.

<p>Relative contribution of each environmental variables to MaxEnt model.</p