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Deployment, Patrolling and Foraging
Published in Yasmina Bestaoui Sebbane, Multi-UAV Planning and Task Allocation, 2020
In another scenario, UAVs begin deployment from a common point, possess no prior knowledge of the environment and operate only under line-of-sight sensing and communication. The objective of the deployment is for the agents to achieve full visibility coverage of the environment while maintaining line-of-sight connectivity with each other. This is achieved by incrementally partitioning the environment into distinct regions, each completely visible from some UAV. Approaches to visibility coverage problems can be divided into two categories: Those where the environment is known a priori, in the art gallery problem, one seeks the smallest set of guards such that every point in a polygon is visible to some guard.Those where the environment must be discovered, simultaneous localization and mapping (SLAM) techniques explore and build a map of the entire environment and then use a centralized procedure to decide where to send agents. For example, deployment locations can be chosen by a human user after an initial map has been built. Waiting for a complete map of the entire environment to be built before placing agents may not be desirable.
Deployment, Patrolling, and Foraging
Published in Yasmina Bestaoui Sebbane, Intelligent Autonomy of Uavs, 2018
In another scenario, UAVs begin deployment from a common point, possess no prior knowledge of the environment, and operate only under line-of-sight sensing and communication. The objective of the deployment is for the agents to achieve full visibility coverage of the environment while maintaining line-of-sight connectivity with each other. This is achieved by incrementally partitioning the environment into distinct regions, each completely visible from some UAV. Approaches to visibility coverage problems can be divided into two categories:Those where the environment is known a priori, in the art gallery problem, one seeks the smallest set of guards such that every point in a polygon is visible to some guard.Those where the environment must be discovered, simultaneous localization and mapping (SLAM) techniques explore and build a map of the entire environment, then use a centralized procedure to decide where to send agents. For example, deployment locations can be chosen by a human user after an initial map has been built. Waiting for a complete map of the entire environment to be built before placing agents may not be desirable.
Lifetime Maximization
Published in Mohamed Ibnkahla, Adaptation and Cross Layer Design in Wireless Networks, 2018
A well-studied area coverage problem is the art gallery problem [25], in which one must determine the number and placement of cameras or guards necessary to cover an art gallery room such that every point in the gallery is observed by at least one camera. This problem can be solved optimally for the two-dimensional case, and is NP-hard in three-dimensional. The art gallery problem is a deterministic coverage problem, since we can control the position of each sensor. In WSN, the locations of the nodes cannot always be controlled due to the nature of their deployment (which might include being dropped from a helicopter, shot out of a cannon, or dispersed from a moving ground vehicle); hence, the notion of random coverage is more pertinent.
Swarm-based painting of an area cluttered with obstacles
Published in International Journal of Parallel, Emergent and Distributed Systems, 2021
Deepanwita Das, Srabani Mukhopadhyaya, Debashis Nandi
Almost all the algorithms discussed above consider robots with unlimited visibility. Considering robots with limited visibility makes the problem more realistic. Fazli et al. [18–21] proposed an algorithm under such a scenario. However, the algorithm assumes that the map of the area and the static obstacles are known to all the robots. Here, all the obstacles are assumed to be line obstacles. The algorithm locates a set of static guards in the target area using the solution of the Art Gallery Problem. It then generates Constrained Delaunay Triangulation (CDT) on the set of static guards and the endpoints of obstacles such that each of the line obstacles must appear in the triangulation as an edge. On CDT, Floyd–Warshall algorithm is used to find shortest paths (as well as shortest distances) between every pair of points. Using these data, a Reduced-CDT (R-CDT) is constructed. To construct R-CDT, the shortest of all shortest paths (provided both endpoints are static guards), along with all its vertices and edges is chosen to be the initial component. This R-CDT is then gradually extended by adding the closest static guard to the current component, along with the complete path by which it is connected to the component. Once R-CDT is computed, the Multi-Prims algorithm is used to decompose the R-CDT graph into a forest of number of partial spanning trees (PSTs), where represents the number of robots. On these sets of PSTs, Constrained Spanning Tour (CST) algorithm is applied to construct a cycle on each PST. Each of these cycles is then allotted to the corresponding robots to cover. This results in the complete coverage of the target area. This algorithm is fault-tolerant in a sense that if at least one robot works correctly, the algorithm guarantees the completeness of coverage. However, though in this work, the robots are assumed to possess a limited range of visibility, knowing the environment completely, amount to assumed that the robots have an unlimited visibility capacity.