The Complexity of Grid Coverage by Swarm Robotics

2011

This paper proposes a mechanism for visually covering an area by means of a group of homogeneous reactive agents through a single-query roadmap called Weighted Multi-Agent RRT, WMA-RRT. While the agents do not know about the environment, the roadmap is locally available to them. In accordance with the swarm intelligence principles, the agents are simple autonomous entities, capable of interacting with the environment by obeying some explicit rules and performing the corresponding actions.

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006

We consider the problem of coverage path planning in an initially unknown or partially known planar environment using multiple robots. Previously, Voronoi partitioning has been proposed as a suitable technique for coverage path planning where the free space in the environment is partitioned into non-overlapping regions called Voronoi cells based on the initial positions of the robots, and one robot is allocated to perform coverage in each region. However, a crucial problem arises if such a partitioning scheme is used in an environment where the location of obstacles is not known a priori-while performing coverage, a robot might perceive an obstacle that occludes its access to portions of its Voronoi cell and this obstacle might prevent the robot from completely covering its allocated region. This would either result in portions of the environment remaining uncovered or requires additional path planning by robots to cover the disconnected regions. To address this problem, we propose a novel algorithm that allows robots to coordinate the coverage of inaccessible portions of their Voronoi cell with robots in neighboring Voronoi cells so that they can repartition the initial Voronoi cells and cover a set of contiguous, connected regions. We have proved analytically that our proposed algorithm guarantees complete, non-overlapping coverage. We have also quantified the performance of our algorithm on e-puck robots within the Webots simulator in different environments with different obstacle geometries and shown that it successfully performs complete, non-overlapping coverage.

2010

Abstract: In this article we present a novel algorithm for uniform coverage of a region. Surveillance, cleaning, and mine detection are some applications that would benefit from this type of algorithm. Prior work has claimed that this task is impossible to solve for non-convex regions. Our algorithm enables robots to uniformly cover arbitrary path-connected regions, such that the robot movements are not predictable and the region periphery is not neglected.

Intelligent Robotics and Applications, 2009

Two algorithms for area coverage (for use in space applications) were evaluated using a simulator and then tested on a multi-robot society consisting of LEGO Mindstorms robots. The two algorithms are (i) a vector force based implementation and (ii) a machine learning approach. The second is based on an organizational-learning oriented classifier system (OCS) introduced by Takadama in 1998.

2016

This paper presents a distributed painting algorithm for painting a priori known rectangular region by swarm of autonomous mobile robots. We assume that the region is obstacle free and of rectangular in shape. The basic approach is to divide the region into some cells, and to let each robot to paint one of these cells. Assignment of different cells to the robots is done by ranking the robots according to their relative positions. In this algorithm, the robots follow the basic Wait-Observe-Compute-Move model together with the Asynchronous timing model. This paper also presents a simulation of the proposed algorithm. The simulation is performed using the Player/Stage Robotic Simulator on Ubuntu 10.04 (Lucid Lynx) platform.

This paper presents a distributed painting algorithm for painting a priori known rectangular region by swarm of autonomous mobile robots. We assume that the region is obstacle free and of rectangular in shape. The basic approach is to divide the region into some cells, and to let each robot to paint one of these cells. Assignment of different cells to the robots is done by ranking the robots according to their relative positions. In this algorithm, the robots follow the basic Wait-Observe-Compute-Move model together with the Asynchronous timing model. This paper also presents a simulation of the proposed algorithm. The simulation is performed using the Player/Stage Robotic Simulator on Ubuntu 10.04 (Lucid Lynx) platform.

Autonomous Robots, 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 Clusterbased 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.

Annals of Mathematics and Artificial Intelligence, 2008

Research in multi robot area coverage, search, and exploration has been receiving consistent attention in recent years, due to the increasing number of real-world applications, such as vacuuming, lawn mowing, demining, surveillance, search and rescue operations, mapping, planetary exploration, etc. All of these applications require that the area of interest be covered by the robots sensors or end-effectors for various purposes. The use of multiple robots potentially provides redundancy and offers opportunities for increasing efficiency. The problem of multi robot area coverage imposes great challenges to researchers in robotics and AI area. This special issue explores the new research frontiers that emerge as new applications are identified and new technologies in robots are introduced. This special issue follows in the footsteps of the highly successful 2001 special issue on coverage. However, the objective of this issue has been broaden to include the adjacent and related fields of multi-robot search and multi-robot exploration. This special issue contains nine research papers that were carefully selected to be both of high quality and to deal with a wide verity of problems related to multi-robot coverage, search, and exploration. The first paper by Rekleitis et al. concerns with the multi-robot coverage problem. The paper presents an algorithm that solves the problem online by sending scout robots to explore the area. The second paper by Agmon, Hazon, and Kaminka deals with the same problem, but here the authors took a different approach by circumnavigating a spanning tree. The third paper by Sarid and Shapiro analyze the competitive complexity of a multi-robot search

We consider boundary coverage of a regular structure by a swarm of miniature robots, and compare a suite of three fully distributed coordination algorithms experimentally. All algorithms rely on boundary coverage by reactive control, whereas coordination of the robots high-level behavior is fundamentally different: random, self-organized, and deliberative with reactive elements. The self-organized coordination algorithm was designed using macroscopic prob-abilistic models that lead to analytical expressions for the algorithm's mean performance. We contrast this approach with a provably complete, near optimal coverage algorithm, which is due to its assumption (noiseless sensors and actuators) infeasible on a real miniature robotic platform, but is considered to yield best-possible policies for an individual robot. Experimental results with swarms of up to 30 robots show that self-organization significantly improves coverage performance with increasing swarm size. We also observe that enforcing a provably complete policy on a miniature robot with limited hardware capabilities is highly sub-optimal as there is a trade-off between coverage throughput and time spent for localization and navigation.