Distributed Infrastructure Inspection Path Planning subject to Time Constraints

TL;DR

This paper presents a novel iterative optimization algorithm for planning inspection paths for aerial robots that efficiently balances inspection reward, time constraints, and vehicle limitations for complex infrastructure.

Contribution

It introduces a new 3-step iterative optimization approach that integrates structure sampling, TSP solving, and inspection time allocation for infrastructure inspection.

Findings

Algorithm achieves high inspection reward within time limits.

Effective handling of diverse structure properties and importance.

Validated through simulations and real-world experiments.

Abstract

Within this paper, the problem of 3D structural inspection path planning for distributed infrastructure using aerial robots that are subject to time constraints is addressed. The proposed algorithm handles varying spatial properties of the infrastructure facilities, accounts for their different importance and exploration function and computes an overall inspection path of high inspection reward while respecting the robot endurance or mission time constraints as well as the vehicle dynamics and sensor limitations. To achieve its goal, it employs an iterative, 3-step optimization strategy at each iteration of which it first randomly samples a set of possible structures to visit, subsequently solves the derived traveling salesman problem and computes the travel costs, while finally it samples and assigns inspection times to each structure and evaluates the total inspection reward. For the derivation of the inspection paths per each independent facility, it interfaces a path planner dedicated to the 3D coverage of single structures. The resulting algorithm properties, computational performance and path quality are evaluated using simulation studies as well as experimental test-cases employing a multirotor micro aerial vehicle.