Publisher: Multidisciplinary Digital Publishing Institute
Project: EC | TAILOR (952215)
Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.
Supporting local and central authorities in decision-making processes pertaining to environmental planning requires the adoption of scientific methods and the submission of proposals that could be implemented in practice. Taking into consideration the dual role that honeybees play as honey producers and crop pollinators, the aim of the present study is to identify and utilize a number of indicators and subsequently develop priority thematic maps. Previous research has focused on the determination of, and, on certain occasions, on mapping, priority areas for apiculture development, based mainly on the needs of honeybees, without taking into consideration the pollination needs of crops that are cultivated in these areas. In addition, research so far has been carried out in specific spatial entities, in contrast to the current study, in which the areas to be comparatively assessed are pre-chosen based on their geographical boundaries. The information derived from this process is expected to help decision-makers in local and regional authorities to adopt measures for optimal land use and sound pollination practices in order to enhance apiculture development at a local scale. To achieve this target, the study incorporates literature about the attractiveness of crops and plants to pollinating honeybees as well as the pollination services provided by honeybees, in combination with detailed vegetative land cover data. The local communities of each municipality were comparatively evaluated, by introducing three indicators through numerical and spatial data analysis: Relative Attractiveness Index (RAI), Relative Dependence Index (RDI), and Relative Priority Index (RPI). Based on these indicators, attractiveness, dependence, and priority maps were created and explained in detail. We suggest that a number of improvement measures that will boost pollination or honey production or both should be taken by decision-makers, based on the correlations between the aforementioned indicators and the exanimated areas. In addition, dependence maps can constitute a powerful tool for raising awareness among both the public and the farmers about the value of honeybees in pollination, thus reinforcing bee protection efforts undertaken globally. Attractiveness maps that provide a thorough picture of the areas that are sources of pollen and nectar can serve as a general guide for the establishment of hives in areas with high potential for beekeeping.
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