Bykerk, L, Quin, P & Liu, D 2019, 'A Method for Selecting the Next Best Angle-of-Approach for Touch-Based Identification of Beam Members in Truss Structures', IEEE Sensors Journal, vol. 19, no. 10, pp. 3939-3949.View/Download from: UTS OPUS or Publisher's site
© 2001-2012 IEEE. A robot designed to climb truss structures such as power transmission towers is expected to have an adequate tactile sensing in the grippers to identify a structural beam member and its properties. Depending on how a gripper grasps a structural member, defined as the Angle-of-Approach (AoA), the extracted tactile data can result in erroneous identifications due to the similarities in beam cross-sectional shapes and sizes. In these cases, further grasps at favorable Angles-of-Approach (AoAs) are required to correctly identify the beam member and its properties. This paper presents an information-based method which uses tactile data to determine the next best AoA for the identification of beam members in truss structures. The method is used in conjunction with a state estimate of beam shape, dimension, and AoA calculated by a Random Forest classifier. The method is verified through simulation by using the data collected using a soft gripper retrofitted with simple tactile sensors. The results show that this method can correctly identify a structural beam member and its properties with a small number of grasps (typically fewer than 4). This method can be applied to other adaptive robotic gripper designs fitted with suitable tactile sensors, regardless of the number of sensors used and their layout.
Rahman, S, Quin, P, Walsh, T, Vidal-Calleja, T, McPhee, MJ, Toohey, E & Alempijevic, A 2018, 'Preliminary estimation of fat depth in the lamb short loin using a hyperspectral camera', Animal Production Science, vol. 58, no. 8, pp. 1488-1496.View/Download from: UTS OPUS or Publisher's site
© 2018 CSIRO. The objectives of the present study were to describe the approach used for classifying surface tissue, and for estimating fat depth in lamb short loins and validating the approach. Fat versus non-fat pixels were classified and then used to estimate the fat depth for each pixel in the hyperspectral image. Estimated reflectance, instead of image intensity or radiance, was used as the input feature for classification. The relationship between reflectance and the fat/non-fat classification label was learnt using support vector machines. Gaussian processes were used to learn regression for fat depth as a function of reflectance. Data to train and test the machine learning algorithms was collected by scanning 16 short loins. The near-infrared hyperspectral camera captured lines of data of the side of the short loin (i.e. with the subcutaneous fat facing the camera). Advanced single-lens reflex camera took photos of the same cuts from above, such that a ground truth of fat depth could be semi-automatically extracted and associated with the hyperspectral data. A subset of the data was used to train the machine learning model, and to test it. The results of classifying pixels as either fat or non-fat achieved a 96% accuracy. Fat depths of up to 12 mm were estimated, with an R 2 of 0.59, a mean absolute bias of 1.72 mm and root mean square error of 2.34 mm. The techniques developed and validated in the present study will be used to estimate fat coverage to predict total fat, and, subsequently, lean meat yield in the carcass.
Quin, P, Paul, G & Liu, D 2017, 'Experimental Evaluation of Nearest Neighbour Exploration Approach in Field Environments', IEEE Transactions on Automation Science and Engineering, vol. 14, no. 2, pp. 869-880.View/Download from: UTS OPUS or Publisher's site
Inspecting surface conditions in 3-D environments such as steel bridges is a complex, time-consuming, and often hazardous undertaking that is an essential part of tasks such as bridge maintenance. Developing an autonomous exploration strategy for a mobile climbing robot would allow for such tasks to be completed more quickly and more safely than is possible with human inspectors. The exploration strategy tested in this paper, called the nearest neighbors exploration approach (NNEA), aims to reduce the overall exploration time by reducing the number of sensor position evaluations that need to be performed. NNEA achieves this by first considering at each time step only a small set of poses near to the current robot as candidates for the next best view. This approach is compared with another exploration strategy for similar robots performing the same task. The improvements between the new and previous strategy are demonstrated through trials on a test rig, and also in field trials on a ferromagnetic bridge structure.
Quin, PD, Paul, G, Alempijevic, A & Liu, D 2016, 'Exploring in 3D with a Climbing Robot: Selecting the Next Best Base Position on Arbitrarily-Oriented Surfaces', Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Daejeon, Korea, pp. 5770-5775.View/Download from: UTS OPUS or Publisher's site
This paper presents an approach for selecting the next best base position for a climbing robot so as to observe the highest information gain about the environment. The robot is capable of adhering to and moving along and transitioning to surfaces with arbitrary orientations. This approach samples known surfaces, and takes into account the robot kinematics, to generate a graph of valid attachment points from which the robot can either move to other positions or make observations of the environment. The information value of nodes in this graph are estimated and a variant of A* is used to traverse the graph and discover the most worthwhile node that is reachable by the robot. This approach is demonstrated in simulation and shown to allow a 7 degree-of-freedom inchworm-inspired climbing robot to move to positions in the environment from which new information can be gathered about the environment.
Paul, G, Quin, P, To, A & Liu, D 2015, 'A Sliding Window Approach to Exploration for 3D Map Building Using a Biologically Inspired Bridge Inspection Robot', Proceedings of the IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, IEEE, Shenyang, China, pp. 1097-1102.View/Download from: UTS OPUS or Publisher's site
This paper presents a Sliding Window approach to viewpoint selection when exploring an environment using a RGB-D sensor mounted to the end-effector of an inchworm climbing robot for inspecting areas inside steel bridge archways which cannot be easily accessed by workers. The proposed exploration approach uses a kinematic chain robot model and information theory-based next best view calculations to predict poses which are safe and are able to reduce the information remaining in an environment. At each exploration step, a viewpoint is selected by analysing the Pareto efficiency of the predicted information gain and the required movement for a set of candidate poses. In contrast to previous approaches, a sliding window is used to determine candidate poses so as to avoid the costly operation of assessing the set of candidates in its entirety. Experimental results in simulation and on a prototype climbing robot platform show the approach requires fewer gain calculations and less robot movement, and therefore is more efficient than other approaches when exploring a complex 3D steel bridge structure.
Paul, G, Quin, P, Yang, C & Liu, D 2015, 'Key Feature-Based Approach for Efficient Exploration of Structured Environments', Proceedings of the 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE International Conference on Robotics and Biomimetics, IEEE, Zhuhai, China, pp. 90-95.View/Download from: UTS OPUS or Publisher's site
This paper presents an exploration approach for robots to determine sensing actions that facilitate the building of surface maps of structured partially-known environments. This approach uses prior knowledge about key environmental features to rapidly generate an estimate of the rest of the environment. Specifically, in order to quickly detect key features, partial surface patches are used in combination with pose optimisation to select a pose from a set of nearest neighbourhood candidates, from which to make an observation of the surroundings. This paper enables the robot to greedily search through a sequence of nearest neighbour poses in configuration space, then converge upon poses from which key features can best be observed. The approach is experimentally evaluated and found to result in significantly fewer exploration steps compared to alternative approaches.
Quin, PD, Alempijevic, A, Paul, G & Liu, D 2014, 'Expanding Wavefront Frontier Detection: An Approach for Efficiently Detecting Frontier Cells', https://ssl.linklings.net/conferences/acra/acra2014_proceedings/views/b…, Australasian Conference on Robotics and Automation, Australasian Robotics and Automation Association, Melbourne, pp. 1-10.View/Download from: UTS OPUS
Frontier detection is a key step in many robot exploration algorithms. The more quickly frontiers can be detected, the more efficiently and rapidly exploration can be completed. This paper proposes a new frontier detection algorithm called Expanding Wavefront Frontier Detection (EWFD), which uses the frontier cells from the previous timestep as a starting point for detecting the frontiers in the current timestep. As an alternative to simply comparing against the naive frontier detection approach of evaluating all cells in a map, a new benchmark algorithm for frontier detection is also presented, called Naive Active Area frontier detection, which operates in bounded constant time. EWFD and NaiveAA are evaluated in simulations and the results compared against existing state-of-the-art frontier detection algorithms, such as Wavefront Frontier Detection and Incremental-Wavefront Frontier Detection.
Ward, PK, Manamperi, P, Brooks, P, Mann, P, Kaluarachchi, W, Matkovic, L, Paul, G, Yang, C, Quin, P, Pagano, D, Liu, D, Waldron, K & Dissanayake, G 2014, 'Climbing Robot for Steel Bridge Inspection: Design Challenges', Proceedings for the Austroads Publications Online, Austroads Bridge Conference, ARRB Group, New South Wales, pp. 1-13.View/Download from: UTS OPUS
Inspection of bridges often requires high risk operations such as working at heights, in confined spaces, in hazardous environments; or sites inaccessible by humans. There is significant motivation for robotic solutions which can carry out these inspection tasks. When inspection robots are deployed in real world inspection scenarios, it is inevitable that unforeseen challenges will be encountered.
Since 2011, the New South Wales Roads & Maritime Services and the Centre of Excellence for Autonomous Systems at the University of Technology, Sydney, have been working together to develop an innovative climbing robot to inspect high risk locations on the Sydney Harbour Bridge. Many engineering challenges have been faced throughout the development of several prototype climbing robots, and through field trials in the archways of the Sydney Harbour Bridge. This paper will highlight some of the key challenges faced in designing a climbing robot for inspection, and then present an inchworm inspired robot which addresses many of these challenges.
Quin, PD, Paul, G, Alempijevic, A, Liu, D & Dissanayake, G 2013, 'Efficient Neighbourhood-Based Information Gain Approach for Exploration of Complex 3D Environments', 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE International Conference on Robotics and Automation, IEEE, Karlsruhe, Germany, pp. 1343-1348.View/Download from: UTS OPUS or Publisher's site
This paper presents an approach for exploring a complex 3D environment with a sensor mounted on the end effector of a robot manipulator. In contrast to many current approaches which plan as far ahead as possible using as much environment information as is available, our approach considers only a small set of poses (vector of joint angles) neighbouring the robot's current pose in configuration space. Our approach is compared to an existing exploration strategy for a similar robot. Our results demonstrate a significant decrease in the number of information gain estimation calculations that need to be performed, while still gathering an equivalent or increased amount of information about the environment.
Quin, PD, Paul, G, Liu, D & Alempijevic, A 2013, 'Nearest Neighbour Exploration with Backtracking for Robotic Exploration of Complex 3D Environments', Proceedings of Australasian Conference on Robotics and Automation, Australasian Conference on Robotics and Automation, Australian Robotics & Automation Association, Sydney, Australia, pp. 1-8.View/Download from: UTS OPUS
Australasian Conference on Robotics and Automation