Multiobjective coverage path planning: Enabling automated inspection of complex, real-world structures

Abstract

An important open problem in robotic planning is the autonomous generation of 3D inspection paths -that is, planning the best path to move a robot along in order to inspect a target structure. We recently suggested a new method for planning paths allowing the inspection of complex 3D structures, given a triangular mesh model of the structure. The method differs from previous approaches in its emphasis on generating and considering also plans that result in imperfect coverage of the inspection target. In many practical tasks, one would accept imperfections in coverage if this results in a substantially more energy efficient inspection path. The key idea is using a multiobjective evolutionary algorithm to optimize the energy usage and coverage of inspection plans simultaneously -and the result is a set of plans exploring the different ways to balance the two objectives. We here test our method on a set of inspection targets with large variation in size and complexity, and compare its performance with two state-of-the-art methods for complete coverage path planning. The results strengthen our confidence in the ability of our method to generate good inspection plans for different types of targets. The method's advantage is most clearly seen for real-world inspection targets, since traditional complete coverage methods have no good way of generating plans for structures with hidden parts. Multiobjective evolution, by optimizing energy usage and coverage together ensures a good balance between the two -both when 100% coverage is feasible, and when large parts of the object are hidden.

A survey on multi-robot coverage path planning for model reconstruction and mapping

“…Additional research work performing model-based CPP and considering the UAV energy are presented in [23,82]. The work presented in [23] proposed multi-objective Evolutionary Algorithm (EA) to generate coverage path for complex structure inspection targeting coverage and energy objectives. The proposed work performs uniform sampling based on an existing model, utilizing a predefined bounding box, and discretization resolution.…”

A Two-Stage Method for Target Searching in the Path Planning for Mobile Robots

“…CPP essentially is used to determine a path that passes all points of an area or volume of interest while avoiding obstacles [1]. CPP has been widely used in many robotic applications, such as vacuum cleaning robots, construction waste recycling robots, autonomous underwater vehicles, creating image mosaics, demining robots, lawn mowers, automated harvesters, window cleaners, and inspection of complex structures [2][3][4][5][6].…”

“…A considerable body of research has studied the CPP problem (e.g., [1,[6][7][8][9]), but relatively little research has been conducted on VCPP. Intuitively speaking, VCPP is a combination of solving a Watchman Route Problem (that is, a problem of computing the shortest route from Point A to Point B in a known area) and an Art Gallery Problem (that is, a problem of selecting the minimum number of observation points to completely observe a known area).…”

A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints

“…In addition, they also ignore the sub-regions in the mission domain, which may be more important and need to be re-covered more frequently. The other class is characterized by approaches which are simple and highly scalable to address the problem of multi-robot persistent coverage [32,33]. These approaches can have better performance than a single robot for persistent coverage, but they do not consider using the least number of the robots to cover the areas.…”