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A survey on coverage path planning for robotics
- Robotics and Autonomous Systems
, 2013
"... Coverage Path Planning (CPP) is the task of de-termining a path that passes over all points of an area or volume of interest while avoiding obsta-cles. This task is integral to many robotic appli-cations, such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image m ..."
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Coverage Path Planning (CPP) is the task of de-termining a path that passes over all points of an area or volume of interest while avoiding obsta-cles. This task is integral to many robotic appli-cations, such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image mosaics, demining robots, lawn mowers, au-tomated harvesters, window cleaners and inspec-tion of complex structures, just to name a few. A considerable body of research has addressed the CPP problem. However, no updated surveys on CPP reflecting recent advances in the field have been presented in the past ten years. In this paper, we present a review of the most successful CPP methods, focusing in the achievements made in the past decade. Furthermore, we discuss reported field applications of the described CPP methods. This work aims to become a starting point for researchers who are initiating their endeavors in CPP. Likewise, this work aims to present a com-prehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works. 1
Multi-Robot Repeated Boundary Coverage Under Uncertainty
"... Abstract—We address the problem of repeated coverage by a team of robots of the boundaries of a target area and the structures inside it. Events may occur on any parts of the boundaries and may have different importance weights. In addition, the boundaries of the area and the structures are heteroge ..."
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Abstract—We address the problem of repeated coverage by a team of robots of the boundaries of a target area and the structures inside it. Events may occur on any parts of the boundaries and may have different importance weights. In addition, the boundaries of the area and the structures are heterogeneous, so that events may appear with varying probabilities on different parts of the boundary, and this probability may change over time. The goal is to maximize the reward by detecting the maximum number of events, weighted by their importance, in minimum time. The reward a robot receives for detecting an event depends on how early the event is detected. To this end, each robot autonomously and continuously learns the pattern of event occurrence on the boundaries over time, capturing the uncertainties in the target area. Based on the policy being learned to maximize the reward, each robot then plans in a decentralized manner to select the best path at that time in the target area to visit the most promising parts of the boundary. The performance of the learning algorithm is compared with a heuristic algorithm for the Travelling Salesman Problem, on the basis of the total reward collected by the team during a finite repeated boundary coverage mission. I.
The Effects of Communication and Visual Range on Multi-Robot Repeated Boundary Coverage
"... Abstract—We address the problem of repeated coverage by a team of robots of the boundaries of a target area and the structures inside it. The robots have limited visual and communication range. Events may occur on any parts of the boundaries and may have different importance weights. In addition, th ..."
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Abstract—We address the problem of repeated coverage by a team of robots of the boundaries of a target area and the structures inside it. The robots have limited visual and communication range. Events may occur on any parts of the boundaries and may have different importance weights. In addition, the boundaries of the area and the structures are heterogeneous, so that events may appear with varying probabilities on different parts of the boundary, and these probabilities may change over time. The goal is to maximize the reward by detecting the maximum number of events, weighted by their importance, in minimum time. The reward a robot receives for detecting an event depends on how early the event is detected. To this end, each robot autonomously and continuously learns the pattern of event occurrence on the boundaries over time, capturing the uncertainties in the target area. Based on the policy being learned to maximize the reward, each robot then plans in a decentralized manner to select the best path at that time in the target area to visit the most promising parts of the boundary. The performance of the learning algorithm is compared with a heuristic algorithm for the Travelling Salesman Problem, on the basis of the total reward collected by the team during a finite repeated boundary coverage mission. Moreover, the effects of robots ’ visual range and communication among the robots on the performance of the proposed algorithms are also investigated.
Multi-Robot Repeated Area Coverage: Performance Optimization Under Various Visual Ranges
"... Abstract—We address the problem of repeated coverage of a target area, of any polygonal shape, by a team of robots having a limited visual range. Three distributed Cluster-based algorithms, and a method called Cyclic Coverage are introduced for the problem. The goal is to evaluate the performance of ..."
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Abstract—We address the problem of repeated coverage of a target area, of any polygonal shape, by a team of robots having a limited visual range. Three distributed Cluster-based algorithms, and a method called Cyclic Coverage are introduced for the problem. The goal is to evaluate the performance of the repeated coverage algorithms under the effect of changes in the robots’ visual range. A comprehensive set of performance metrics are considered, including the distance the robots travel, the frequency of visiting points in the target area, and the degree of balance in workload distribution among the robots. The Cyclic Coverage approach, used as a benchmark to compare the algorithms, produces optimal or near-optimal solutions for the single robot case under some criteria. The results show that the identity of the optimal repeated coverage algorithm depends on the metric and the robots ’ visual range.
Multi-robot repeated area coverage
, 2013
"... We address the problem of repeated coverage of a target area, of any polygonal shape, by a team of robots having a limited visual range. Three distributed Cluster-based algorithms, and a method called Cyclic Coverage are introduced for the problem. The goal is to evaluate the performance of the re ..."
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Cited by 1 (0 self)
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We address the problem of repeated coverage of a target area, of any polygonal shape, by a team of robots having a limited visual range. Three distributed Cluster-based algorithms, and a method called Cyclic Coverage are introduced for the problem. The goal is to evaluate the performance of the repeated coverage algorithms under the effects of the variables: Environment Representation, and the Robots ’ Visual Range. A comprehensive set of performance metrics are considered, including the distance the robots travel, the frequency of visiting points in the target area, and the degree of balance in workload distribution among the robots. The Cyclic Coverage approach, used as a benchmark to compare the algorithms, produces optimal or near-optimal solutions for the single robot case under some criteria. The results can be used as a framework for choosing an appropriate combination of repeated coverage algorithm, environment representation, and the robots ’ visual range based on the particular scenario and the metric to be optimized.
Sampling-Based Coverage . . . Structures
, 2012
"... Path planning is an essential capability for autonomous robots, and many applica-tions impose challenging constraints alongside the standard requirement of obsta-cle avoidance. Coverage planning is one such task, in which a single robot must sweep its end effector over the entirety of a known worksp ..."
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Path planning is an essential capability for autonomous robots, and many applica-tions impose challenging constraints alongside the standard requirement of obsta-cle avoidance. Coverage planning is one such task, in which a single robot must sweep its end effector over the entirety of a known workspace. For two-dimensional environments, optimal algorithms are documented and well-understood. For three-dimensional structures, however, few of the available heuristics succeed over occluded regions and low-clearance areas. This thesis makes several contributions to sampling-based coverage path planning, for use on complex three-dimensional structures. First, we introduce a new algorithm for planning feasible coverage paths. It is more computationally efficient in problems of complex geometry than the well-known dual sampling method, especially when high-quality solutions are desired. Second, we present an improvement procedure that iteratively shortens and smooths a feasible coverage path; robot configurations are adjusted without violating any coverage con-straints. Third, we propose a modular algorithm that allows the simple components
Sampling-Based Coverage Path Planning for Complex 3D Structures
, 2012
"... Path planning is an essential capability for autonomous robots, and many applica-tions impose challenging constraints alongside the standard requirement of obsta-cle avoidance. Coverage planning is one such task, in which a single robot must sweep its end effector over the entirety of a known worksp ..."
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Path planning is an essential capability for autonomous robots, and many applica-tions impose challenging constraints alongside the standard requirement of obsta-cle avoidance. Coverage planning is one such task, in which a single robot must sweep its end effector over the entirety of a known workspace. For two-dimensional environments, optimal algorithms are documented and well-understood. For three-dimensional structures, however, few of the available heuristics succeed over occluded regions and low-clearance areas. This thesis makes several contributions to sampling-based coverage path planning, for use on complex three-dimensional structures. First, we introduce a new algorithm for planning feasible coverage paths. It is more computationally efficient in problems of complex geometry than the well-known dual sampling method, especially when high-quality solutions are desired. Second, we present an improvement procedure that iteratively shortens and smooths a feasible coverage path; robot configurations are adjusted without violating any coverage con-straints. Third, we propose a modular algorithm that allows the simple components
Measuring Variables Effect to Statistically Model the Multi-Robot Patrolling Problem by Means of ANOVA
"... Abstract. This paper focuses on analyzing extensive results generated from running diverse multi-robot patrolling algorithms with different configurations towards measuring the influence of the variables of the general problem. In order to do this, a statistical technique by the name of Analysis of ..."
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Abstract. This paper focuses on analyzing extensive results generated from running diverse multi-robot patrolling algorithms with different configurations towards measuring the influence of the variables of the general problem. In order to do this, a statistical technique by the name of Analysis of Variance (ANOVA) is employed to compare the parameters and identify the ones which give raise to the total dispersion of the data set, at the same time accessing their contribution to the obtained results. It is shown that by applying such technique, it is possible to compute a data-related model for the Multi-Robot Patrolling
Cooperative
"... large area surveillance with a team of aerial mobile robots for long endurance missions ..."
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large area surveillance with a team of aerial mobile robots for long endurance missions
Autonomous Robots
, 2012
"... Efficient complete coverage of a known arbitrary environment with applications to ..."
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Efficient complete coverage of a known arbitrary environment with applications to
