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Area Coverage With Multiple Capacity-Constrained Robots
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AI
DOI：10.1109/LRA.2022.3146952.png)
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Abstract
En 中文
The area coverage problem is the task of efficiently servicing a given two-dimensional surface using sensors mounted on robots such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). We present a novel formulation for generating coverage routes for multiple capacity-constrained robots, where capacity can be specified in terms of battery life or flight time. Traversing the environment incurs demands on the robot resources, which have capacity limits. The central aspect of our approach is transforming the area coverage problem into a line coverage problem (i.e., coverage of linear features), and then generating routes that minimize the total cost of travel while respecting the capacity constraints. We define two modes of travel: (1) servicing and (2) deadheading, which correspond to whether a robot is performing task-specific actions or not. Our formulation allows separate and asymmetric travel costs and demands for the two modes. Furthermore, the cells computed from cell decomposition, aimed at minimizing the number of turns, are not required to be monotone polygons. We develop new procedures for cell decomposition and generation of service tracks that can handle non-monotone polygons with or without holes. We establish the efficacy of our algorithm on a ground robot dataset with 25 indoor environments and an aerial robot dataset with 300 outdoor environments. The algorithm generates solutions whose costs are 10% lower on average than state-of-the-art methods. We additionally demonstrate our algorithm in experiments with UAVs.
Keywords:
Robots
Robot sensing systems
Costs
Task analysis
Sensors
Batteries
Autonomous aerial vehicles
Path planning for multiple mobile robots or agents
aerial systems
applications
computational geometry
area coverage
line coverage
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