A survey on multi-robot coverage path planning for model reconstruction and mapping (original) (raw)

A framework for multi-robot coverage analysis of large and complex structures

Journal of Intelligent Manufacturing, 2021

Coverage analysis is essential for many coverage tasks (e.g., robotic grit-blasting, painting and surface cleaning) performed by Autonomous Industrial Robots (AIRs). Coverage analysis enables (1) the performance evaluation (e.g., coverage rate and operation efficiency) of AIRs for a coverage task, and (2) the configuration design of a multi-AIR system (e.g., decision on the number of AIRs to be used). Multi-AIR coverage analysis of large and complex structures involves addressing various problems. Thus, a framework is presented in this paper that incorporates various modules (e.g., AIR reachability, AIR base placement, collision avoidance, and area partitioning and allocation) for appropriately addressing the associated problems. The modules within the framework provide the flexibility of utilizing different methods and algorithms, depending on the requirements of the target application. The framework is tested and validated by extensive analyses of 10 different scenarios with up-to 10 AIRs.

Multi-Robot Simultaneous Coverage and Mapping of Complex Scene - Comparison of Different Strategies

2018

This paper addresses the problem of optimizing the observation of a human scene using several mobile robots. Mobile robots have to cooperate to find a position around the scene maximizing its coverage. The scene coverage is defined as the observation of the human pose skeleton. It is assumed that the robots can communicate but have no map of the environment. Thus the robots have to simultaneously cover and map the scene and the environment. We consider an incremental approach to master state-space complexity. Robots build an hybrid metric-topological map while evaluating the observation of the human pose skeleton. To this end we propose and evaluate different online optimization strategies exploiting local versus global information. We discuss the difference of the performance and cost. Experiments are performed both in simulation and with real robots.

Techniques in Multi-Robot Area Coverage

Advances in computational intelligence and robotics book series, 2016

Swarm of mobile robots is actually a large number of small robots imitating the behavior of social insects that perform certain tasks in a group. This chapter considers the problem of area coverage by a swarm of mobile robots. Initially, the robots occupy random positions in a target area. The objective is to physically cover/scan each accessible location of that area by at least one robot of the swarm. After discussing, in brief, different models and their challenges this chapter summarizes the research works carried out to solve this problem. The existing literature is classified into two categories, namely, team based approach and individual approach. The pros and cons of both the approaches are indicated and finally a comparative study of the addressed works in terms of computational model, synchrony, characteristics of robots, etc. is presented.

MR-SimExCoverage: Multi-robot Simultaneous Exploration and Coverage

Computers & Electrical Engineering, 2020

In this paper, we present a novel problem of simultaneous exploration and area coverage by multiple cooperating mobile robots. As the robots cover an initially unknown region, they perform intermittent exploration of the region and build a map, which in turn is used to plan the coverage path. We use a Voronoi partition based multi-robot coverage strategy using the Manhattan distance metric to solve the coverage problem and a frontier based exploration strategy for exploration mapping. We provide results of simulation using Matlab/V-rep environments to demonstrate the proposed multi-robot simultaneous exploration and coverage (MR-SimExCoverage) problem using the spanning tree based coverage (STC) algorithm.

Multi-robot 3d coverage of unknown terrains

Decision and Control …, 2011

In this paper we study the problem of deploying a team of flying robots to perform surveillance coverage missions over an unknown terrain of arbitrary morphology. In such a mission, the robots should simultaneously accomplish two objectives: firstly, to make sure that the overall terrain is visible by the team and, secondly, that the distance between each point in the terrain and one of the robots is as small as possible. These two objectives should be efficiently fulfilled given the physical constraints and limitations imposed at the particular coverage application (i.e., obstacle avoidance, limited sensor capabilities, etc). As the terrain's morphology is unknown and it can be quite complex and non-convex, standard multi-robot coordination and control algorithms are not applicable to the particular problem treated in this paper. In order to overcome such a problem, a new approach that is based on the Cognitivebased Adaptive Optimization (CAO) algorithm is proposed and evaluated in this paper. Both rigorous mathematical arguments and extensive simulations on unknown terrains establish that the proposed approach provides an efficient methodology that can easily incorporate any particular constraints and quickly and safely navigate the robots to an arrangement that optimizes surveillance coverage.

Efficient Boustrophedon Multi-Robot Coverage: an algorithmic approach

Annals of Mathematics and Artificial Intelligence, 2008

This paper presents algorithmic solutions for the complete coverage path planning problem using a team of mobile robots. Multiple robots decrease the time to complete the coverage, but maximal efficiency is only achieved if the number of regions covered multiple times is minimized. A set of multi-robot coverage algorithms is presented that minimize repeat coverage. The algorithms use the same planar cellbased decomposition as the Boustrophedon single robot coverage algorithm, but provide extensions to handle how robots cover a single cell, and how robots are allocated among cells. Specifically, for the coverage task our choice of multi-robot policy strongly depends on the type of communication that exists between the robots. When the robots operate under the line-of-sight communication restriction, keeping them as a team helps to minimize repeat coverage. When communication between the robots is available without any restrictions, the robots are initially distributed through space, and each one is allocated a virtually-bounded area to cover. A greedy auction mechanism is used for task/cell allocation among the robots. Experimental results from different simulated and real environments that illustrate our approach for different communication conditions are presented.

Remember-All Based Frontier Allocation for Multi-Robot Coverage in Unknown Environments

2016

Robots are being increasingly used for coverage tasks which were earlier considered too dangerous or monotonous to be performed by humans such as interplanetary exploration, search & rescue missions, etc. Out of all the multi-robot coverage approaches, the frontier based approach is one of the most widely used. Most of the coverage approaches developed so far face the issue of frontier duplication and require access to maps of the environments prior to coverage. In this work, a new frontier based strategy for multi-robot coverage in unknown environments is developed. This new strategy tries to remember and manage all the frontiers discovered so far. It is scalable to multiple robots and does not require prior access to the maps. It also uses a new frontier allocation and coordination strategy, which reduces the frontier duplication and improves the efficiency of coverage. Keywords-robot coverage; autonomous robots; frontier coverage; unknown environments; ROS

Online Multi-Robot Coverage: Algorithm Comparisons

2018

We consider the common assumptions made when multi-robot systems are used for exploration and coverage and the metrics used to compare performance. We then take three algorithms -- the Rolling Dispersion Algorithm (RDA), the Multi-Robot Depth-First-Search (MR-DFS) algorithm, and the BoB algorithm -- chosen for their different strengths and assumptions, and compare, using a set of common metrics, their performance in different simulation environments. We present two simple extensions to RDA -- RDA-MS (multi-start) and RDA-EC (extended communication), which preserve RDA's original assumptions, but are able to perform as well as the algorithms that make more demanding assumptions.

A Common Optimization Framework for Multi-Robot Exploration and Coverage in 3D Environments

Journal of Intelligent & Robotic Systems, 2020

This paper studies the problems of static coverage and autonomous exploration of unknown three-dimensional environments with a team of cooperating aerial vehicles. Although these tasks are usually considered separately in the literature, we propose a common framework where both problems are formulated as the maximization of online acquired information via the definition of single-robot optimization functions, which differs only slightly in the two cases to take into account the static and dynamic nature of coverage and exploration respectively. A common derivative-free approach based on a stochastic approximation of these functions and their successive optimization is proposed, resulting in a fast and decentralized solution. The locality of this methodology limits however this solution to have local optimality guarantees and specific additional layers are proposed for the two problems to improve the final performance. Specifically, a Voronoi-based initialization step is added for the coverage problem and a combination with a frontier-based approach is proposed for the exploration case. The resulting algorithms are finally tested in simulations and compared with possible alternatives.

Fault-tolerant multi-robot area coverage with limited visibility

2010

Abstract—We address the problem of multi-robot area coverage and present a new approach in the case where the map of the area and its static obstacles are known and the robots have a limited visibility range. The proposed method starts by locating a set of static guards on the map of the target area and then builds a graph called Reduced-CDT, a new environment representation method based on the Constrained Delaunay Triangulation (CDT).