Publications

Carmichael, MG, Tran, A, Aldini, S & Liu, D 2024, 'Methodologies in Physical Human–Robot Collaboration for Infrastructure Maintenance', Wiley, pp. 105-126.
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Hassan, M & Liu, D 2024, 'Coverage Planning and Motion Planning of Intelligent Robots for Civil Infrastructure Maintenance', Wiley, pp. 81-103.
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Huang, S, Pan, K & Dissanayake, G 2024, 'Perception in Complex and Unstructured Infrastructure Environments', Wiley, pp. 33-58.
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Isaac, G, Nicholas, P, Paul, G, Pietroni, N, Vidal Calleja, T, Xie, M & Schork, T 2024, 'Automated Shotcrete: A More Sustainable Construction Technology' in Sustainable Engineering, Springer International Publishing, pp. 331-345.
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Kodagoda, S, Kumar Viswanathan, V, Thiyagarajan, K, Tran, A, Wickramanayake, S, Barclay, S & Vitanage, D 2024, 'Robotics and Sensing for Condition Assessment of Wastewater Pipes', Wiley, pp. 243-261.
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Liu, D & Dissanayake, G 2024, 'Infrastructure Robotics: An Introduction', Wiley, pp. 3-18.
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Valls Miro, J 2024, 'Robots for Underground Pipe Condition Assessment', Wiley, pp. 221-242.
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Best, G, Garg, R, Keller, J, Hollinger, GA & Scherer, S 2024, 'Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy', The International Journal of Robotics Research, vol. 43, no. 4, pp. 485-512.
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We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard teams of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the benefits of both visual and range sensors with a generalized frontier-based exploration algorithm and an OpenVDB-based map processing pipeline. Our local planner utilizes a dynamically feasible trajectory library and a GPU-based Euclidean distance transform map to allow fast and safe navigation through both tight doorways and expansive spaces. The autonomy pipeline is evaluated with an extensive set of field experiments, with teams of up to three robots that fly up to 3 m/s and distances exceeding 1 km in confined spaces. We provide a summary of various field experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. Experiments are also detailed from the DARPA Subterranean Challenge, where our proposed autonomy pipeline contributed to us winning the “Most Sectors Explored” award. We provide an extended discussion of lessons learned, release software as open source, and present a video that illustrates our extensive field trials.

Hernandez Moreno, V, Jansing, S, Polikarpov, M, Carmichael, MG & Deuse, J 2024, 'Obstacles and opportunities for learning from demonstration in practical industrial assembly: A systematic literature review', Robotics and Computer-Integrated Manufacturing, vol. 86, pp. 102658-102658.
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Huang, S, Hauser, K & Shell, DA 2024, 'Selected papers from RSS2022', The International Journal of Robotics Research, vol. 43, no. 4, pp. 387-388.
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John, AR, Singh, AK, Gramann, K, Liu, D & Lin, C-T 2024, 'Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human–robot collaboration', Journal of Neural Engineering, vol. 21, no. 2, pp. 026010-026010.
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Abstract Objective. Brain–computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC. Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials. Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed a...

Kim, S, Corah, M, Keller, J, Best, G & Scherer, S 2024, 'Multi-Robot Multi-Room Exploration With Geometric Cue Extraction and Circular Decomposition', IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1190-1197.
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Li, T, Walker, P, Khonasty, R, van de Graaf, VA, Yelf, E, Zhao, L & Huang, S 2024, 'Robotic‐assisted burring in total hip replacement: A new surgical technique to optimise acetabular preparation', The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 20, no. 1.
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AbstractBackgroundIn Total Hip replacement (THR) surgery, a critical step is to cut an accurate hemisphere into the acetabulum so that the component can be fitted accurately and obtain early stability. This study aims to determine whether burring rather than reaming the acetabulum can achieve greater accuracy in the creation of this hemisphere.MethodsA preliminary robotic system was developed to demonstrate the feasibility of burring the acetabulum using the Universal Robot (UR10). The study will describe mechanical design, robot trajectory optimisation, control algorithm development, and results from phantom experiments compared with both robotic reaming and conventional reaming. The system was also tested in a cadaver experiment.ResultsThe proposed robotic burring system can produce a surface in 2 min with an average error of 0.1 and 0.18 mm, when cutting polyurethane bone block #15 and #30, respectively. The performance was better than robotic reaming and conventional hand reaming.ConclusionThe proposed robotic burring system outperformed robotic and conventional reaming methods to produce an accurate acetabular cavity. The findings show the potential usage of a robotic‐assisted burring in THR for acetabular preparation.

Ma, W, Huang, S & Sun, Y 2024, 'Triplet-Graph: Global Metric Localization Based on Semantic Triplet Graph for Autonomous Vehicles', IEEE Robotics and Automation Letters, vol. 9, no. 4, pp. 3155-3162.
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Wang, H, Zhao, X, Huang, S, Li, Q & Liu, Y 2024, 'A branch-and-bound based globally optimal solution to 2D multi-robot relative pose estimation problems', Automatica, vol. 164, pp. 111654-111654.
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Young, MW, Webster, C, Tanis, D, Schurr, AF, Hanna, CS, Lynch, SK, Ratkiewicz, AS, Dickinson, E, Kong, FH & Granatosky, MC 2024, 'What does climbing mean exactly? Assessing spatiotemporal gait characteristics of inclined locomotion in parrots', Journal of Comparative Physiology A, vol. 210, no. 1, pp. 19-33.
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Nguyen, K, Alempijevic, A & Paul, G 1970, 'Decentralized multi-phase formation control for cattle herding', IEEE International Conference on Robotics and Automation, Yokohama, Japan.

Rees, N, Thiyagarajan, K & Kodagoda, S 1970, 'Robotic Guide Dog for Real-time Indoor Object Detection and Classification with Localization', 2024 IEEE Applied Sensing Conference (APSCON), 2024 IEEE Applied Sensing Conference (APSCON), IEEE.
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