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Orienteering and Coverage
Published in Yasmina Bestaoui Sebbane, Multi-UAV Planning and Task Allocation, 2020
A strategic level regarding the choice of the way-points is made before the operational decisions regarding routing of UAVs between the different way-points. These two decisions are strongly related by factors such as UAV range and endurance, topography and communications, and the mission requirements. Different approaches have been developed such as a method that creates a spanning tree and generates the coverage paths as the boundary around it. Many different types of basic path patterns have been proposed for coverage algorithms. The most common patterns are parallel swath, also known as parallel milling, or zigzag patterns. The seed spreader algorithm describes an efficient, deterministic and complete coverage strategy for simple regions, by having the robot move in back and forth, or lawnmower motion, or sweeping motions. The following standard search patterns are as follows: Lawnmower Search that consists in flying along straight lines with 180 degree turns at the end. Based on the sweep direction, there are two types of lawnmowers: Parallel track search if the search area is large and level, only the approximate location of the target is known and uniform coverage is desired.Creeping line search if the search area is narrow and long and the probable location of the target is thought to be on either side of the search track.Spiral search and Expanding square search if the search area is small and the position of the target is known within close limits.Sector search used similarly to the expanding square search; it offers several advantages: concentrated coverage near the center of the search area, easier to fly than the expanding square search and view of the search area from many angles;Contour search used to patrol obstacles, often assumed to be polygonal.
The reliability and transparency bases of trust in human-swarm interaction: principles and implications
Published in Ergonomics, 2020
Aya Hussein, Sondoss Elsawah, Hussein A. Abbass
A swarm of 20 unmanned aerial vehicles (UAVs) is deployed in a grid-based environment. The objective of the mission is to collect the maximum number of target objects which are scattered in initially hidden positions in the environment. The environment contains 60 target objects (the collection of each is rewarded by +5 points) and sixty non-target objects whose collection does not result in a win or a loss of any points. The swarm navigates the environment autonomously by applying the flocking algorithm (Reynolds 1987). The swarm moves in a lawn mower pattern, as seen in Figure 2, searching for target objects. This simple navigation has been chosen as the focus of our study is on swarm decision-making rather than its navigation. Algorithms for robot mowing have been presented in Oleson (2003).
Integrating four-dimensional ontology and systems requirements modelling
Published in Journal of Engineering Design, 2019
The problem can be addressed with separate but integrated layers for engineering and logical languages (Bock et al. 2010; Bock and Odell 2011). Engineers use the layer devoted to their discipline, while the logical layer is inferred without engineers being directly aware of it (the integration itself is logical). For example, an engineering layer might provide a way to specify which product designs are intended to satisfy which requirements. Logically speaking, requirements and designs are classifications of real, imagined, or simulated things that meet particular membership conditions (specifications). The difference is requirements place fewer conditions on things than designs do, which is the definition of logical subsumption. For example, a product requirement for a lawn mower would be that it cut grass, while a design satisfying this requirement might specify it is done with a rotating blade and electric motor. Engineers use the notion of requirement satisfaction in the engineering layer, with subsumption automatically inferred from it in the logical layer without engineers being aware of it. Reasoners can operate on the logical layer, with results translated back to engineering. For example, if a reasoner finds requirement conditions do not actually subsume design conditions as claimed, then this can be communicated to the engineer in terms of requirement (dis)satisfaction, rather than subsumption.
Neural network-based nonlinear sliding-mode control for an AUV without velocity measurements
Published in International Journal of Control, 2019
Xinxin Guo, Weisheng Yan, Rongxin Cui
Before designing a controller for an AUV, path planning is implemented. To reach a certain point, Dubins curve and Helix curve are generally used to construct the trajectory (Wang, Wang, Tan, Zhou, & Wei, 2015). In the coverage control, the lawn-mower route is generally adopted (Song & Arshad, 2016). To clearly illustrate the performance of the three control strategies in the time domain, the step reference trajectory for the heave motion is selected, which can also be regarded as a simplified lawn-mower route for an AUV. The spiral reference trajectory is selected to verity the three-dimensional tracking performance considering the perception range of ODIN sensors, which is also a typical route for an AUV.