Enabling Humans to Plan Inspection Paths Using a Virtual Reality Interface

In this work, we investigate whether humans can manually generate high-quality robot paths for optical inspections. Typically, automated algorithms are used to solve the inspection planning problem. The use of automated algorithms implies that specialized knowledge from users is needed to set up the algorithm. We aim to replace this need for specialized experience, by entrusting a non-expert human user with the planning task. We augment this user with intuitive visualizations and interactions in virtual reality. To investigate if humans can generate high-quality inspection paths, we perform a user study in which users from different experience categories, generate inspection paths with the proposed virtual reality interface. From our study, it can be concluded that users without experience can generate high-quality inspection paths: The median inspection quality of user generated paths ranged between 66-81\% of the quality of a state-of-the-art automated algorithm on various inspection planning scenarios. We noticed however, a sizable variation in the performance of users, which is a result of some typical user behaviors. These behaviors are discussed, and possible solutions are provided.

[1]  Joachim Denzler,et al.  An Information Theoretic Approach for Next Best View Planning in 3-D Reconstruction , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[2]  Christos Papachristos,et al.  Augmented reality-enhanced structural inspection using aerial robots , 2016, 2016 IEEE International Symposium on Intelligent Control (ISIC).

[3]  Olga Sorkine-Hornung,et al.  Instant field-aligned meshes , 2015, ACM Trans. Graph..

[4]  Michael A. Goodrich,et al.  Informative path planning with a human path constraint , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[5]  Wolfgang Stuerzlinger,et al.  An Evaluation of Interactive and Automated Next Best View Methods in 3D Scanning , 2013 .

[6]  Clément Gosselin,et al.  Intuitive Adaptive Orientation Control for Enhanced Human–Robot Interaction , 2019, IEEE Transactions on Robotics.

[7]  Boris Bogaerts,et al.  Optimized dynamic line scanning thermography for aircraft structures , 2019 .

[8]  Surya P. N. Singh,et al.  V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Franz S. Hover,et al.  Sampling-Based Coverage Path Planning for Inspection of Complex Structures , 2012, ICAPS.

[10]  Yevgeniy Vorobeychik,et al.  Submodular Optimization with Routing Constraints , 2016, AAAI.

[11]  Douglas Vickers,et al.  Human Performance on Visually Presented Traveling Salesperson Problems with Varying Numbers of Nodes , 2006, J. Probl. Solving.

[12]  Chandra Chekuri,et al.  A recursive greedy algorithm for walks in directed graphs , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).

[13]  Andreas Krause,et al.  Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies , 2008, J. Mach. Learn. Res..

[14]  Rudi Penne,et al.  Near-Optimal Path Planning for Complex Robotic Inspection Tasks , 2019, ArXiv.

[15]  Rudi Penne,et al.  Interactive Camera Network Design Using a Virtual Reality Interface , 2019, Sensors.

[16]  Roland Siegwart,et al.  Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots , 2015, Autonomous Robots.

[17]  Jonathan Fink,et al.  Shaping of Shared Autonomous Solutions With Minimal Interaction , 2018, Front. Neurorobot..

[18]  Jean-Pierre Kruth,et al.  Automated dimensional inspection planning using the combination of laser scanner and tactile probe , 2012 .

[19]  Mohammadreza Aghaei,et al.  Light Unmanned Aerial Vehicles (UAVs) for Cooperative Inspection of PV Plants , 2014, IEEE Journal of Photovoltaics.

[20]  Rudi Penne,et al.  A Gradient-Based Inspection Path Optimization Approach , 2018, IEEE Robotics and Automation Letters.

[21]  Gaurav S. Sukhatme,et al.  Branch and bound for informative path planning , 2012, 2012 IEEE International Conference on Robotics and Automation.

[22]  Carmelo Mineo,et al.  Robotic path planning for non-destructive testing – A custom MATLAB toolbox approach , 2016 .