Coverage First Next Best View for Inspection of Cluttered Pipe Networks Using Mobile Manipulators

2026-03-17

AI Summary

This work addresses the challenge of achieving full coverage inspection of pipe networks while avoiding collisions with unknown obstacles in confined, cluttered environments. The authors propose a “coverage-first” next-best-view planning approach that prioritizes coverage as the primary optimization objective, reformulating the problem as a parallel exploration–exploitation strategy to maximize information gain. Their method innovatively integrates stochastic geometric primitives for online environment modeling and introduces chance-constrained extensions to vector field inequalities to ensure safe navigation under perceptual uncertainty. Experimental results demonstrate that the proposed framework autonomously generates inspection paths achieving complete coverage in complex, constrained scenarios, successfully reconstructing simplified pipe networks while enabling real-time obstacle avoidance.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Robotic inspection of radioactive areas enables operators to be removed from hazardous environments; however, planning and operating in confined, cluttered environments remain challenging. These systems must autonomously reconstruct the unknown environment and cover its surfaces, whilst estimating and avoiding collisions with objects in the environment. In this paper, we propose a new planning approach based on next-best-view that enables simultaneous exploration and exploitation of the environment by reformulating the coverage path planning problem in terms of information gain. To handle obstacle avoidance under uncertainty, we extend the vector-field-inequalities framework to explicitly account for stochastic measurements of geometric primitives in the environment via chance constraints in a constrained optimal control law. The stochastic constraints were evaluated experimentally alongside the planner on a mobile manipulator in a confined environment to inspect a pipe network. These experiments demonstrate that the system can autonomously plan and execute inspection and coverage paths to reconstruct and fully cover the simplified pipe network. Moreover, the system successfully estimated geometric primitives online and avoided collisions during motion between viewpoints.

Problem

• coverage path planning
• next-best-view
• mobile manipulators
• cluttered environments
• collision avoidance

Innovation

• next-best-view
• coverage path planning
• chance-constrained control
• vector-field inequalities
• mobile manipulator