Herding is performed by people or trained animals to control the movement of livestock under the desired direction of an operator. This paper presents a novel decentralized control strategy for a group of robots to herd animals which consists of two phases, a surrounding phase and a driving phase. In the surrounding phase, a custom artificial potential field is employed to simultaneously guide the robots to encircle the herd by tracking the outmost animals and maintaining a safe distance from other neighboring robots. Once the encirclement is complete, the robots transition to drive the animals toward a designated goal by simply maintaining their initial formation and traversing to it. Unlike existing works on herding using flocking control, local observations of the nearest animals and communication with other robots within the sensing range are the only requirements for the robots to surround and herd the animals effectively. Moreover, the animal-robot behavior model resembles the interaction of livestock in the presence of an external predatory threat, where robots act as predators. An analytical proof and empirical results collected from different simulators demonstrate that the proposed control enables the robots to converge around the boundary of the animals and guide them toward the designated goal.

This poster investigates the use of point cloud processing algorithms to provide annotations for robotic manipulation tasks completed remotely via Virtual Reality (VR). A VR-based system has been developed that receives and visualizes the processed data from real-time RGB-D camera feeds. A real-world robot model has also been developed to provide realistic reactions and control feedback. The targets and the robot model are reconstructed in a VR environment and presented to users in different modalities. The modalities and available information are varied between experimental settings, and the associated task performance is recorded and analyzed. The results accumulated from 192 experiments completed by 8 participants showed that point cloud data is sufficient for completing the task. Additional information, either image stream or preliminary processes presented as annotations, was found to not have a significant impact on the completion time. However, the combination of image stream and colored point cloud data visualization modalities was found to greatly enhance a user’s performance accuracy, with the number of target centers missed being reduced by 40%.

Additive Manufacturing, has evolved beyond prototyping to manufacturing end-products. The authors are involved in developing a large-scale extrusion-based 3D printer to print mining equipment - a Gravity Separation Spiral, and embedding sensors to monitor the operational conditions re-motely. This paper presents a temperature-compensated strain sensor that can be 3D printed inline within large-scale 3D printed equipment. The sensor is printed using conductive carbon filament and embedded in a Polylactic acid (PLA) base. A half-bridge setup is proposed to reduce the impact of temperature variations. Temperature-controlled tests have been conducted with the proposed half-bridge and compared with a non-temperature compensated quarter-bridge setup. Results show that the half-bridge configuration reduces the temperature impact on the strain measurement significantly (68%) compared to the quarter-bridge, in the range of 25-40 °C. Deflection testing conducted on the printed sensor shows a near-linear relationship between bending strain and voltage. Multiple bending cycles have shown that there is no significant hysteresis. ANSYS simulations are used to accurately estimate the internal temperature since embedding a temperature sensor would affect the structural integrity. Although carbon black material is naturally brittle, steps have been taken in the design to avoid undesirable cracking. Results from laser microscopy analysis of the printed traces showed no crack defects.

This paper investigates the effect of real-time sensor data variances on humans performing straightforward assembly tasks in a Virtual Reality-based (VR-based) training system. A VR-based training system has been developed to transfer color and depth images, and constructs colored point clouds data to represent objects in real-time. Various parameters that affect sensor data acquisition and visualization of remotely operated robots in the real-world are varied. Afterward, the associated task performance is observed. Experimental results from 12 participants performed a total of 95 VR-guided puzzle assembly tasks demonstrated that a combination of low resolution and uncolored points has the most significant effect on participants’ performance. Participants mentioned that they needed to rely upon tactile feedback when the perceptual feedback was minimal. The most insignificant parameter determined was the resolution of the data representations, which, when varied within the experimental bounds, only resulted in a 5% average change in completion time. Participants also indicated in surveys that they felt their performance had improved and frustration was reduced when provided with color information of the scene.

This paper presents an integration of Virtual Reality (VR) interfaces with the control system of a realworld mobile manipulator, ultimately facilitating a natural and intuitive method for human-robot interaction. VR's ability to track movements in 3D space and translate performed motions provide an intuitive platform for users to explore and interact with the virtual environment. Coupled with intuitive controls, such as grabbing and pointing, the VR platform provides a compelling advantage that can be used to solve limitations of traditional remote robot teleoperation methods. This paper summarises the system implemented, which includes a simulation of the robot in Unity3d, as well as analyses critical results of accuracy and performance, from experiments with users of various experience levels. The method used for measuring accuracy with a simulated robot presented a utilitarian validation for contrasting the difference between 2D and VR 3D interfaces. Users' performance and experience under various levels of control latency, which is a crucial factor in remote online robot control, were also measured.

The world is embracing the fourth industrial revolution, Industry 4.0, which is enabling businesses to improve efficiency and optimise operations. The authors are part of a team that is researching and developing a large-scale industrial 3D printer to print smart, bespoke equipment called Gravity Separation Spirals (GSS). GSS are used in mining to separate minerals from the slurry. The printer under development employs two industrial robot arms mounted on vertical rails and the print direction is around a vertical rotating column in a radial direction. This paper presents a cost-based path planning method using print-space sampling to optimise distance error and manipulability during a printhead's radial path as it travels outwards from the central column. Manipulability, distance error and rotation error have been calculated for each sampled point and a weighted cost function has been used to determine the optimal path. Simulated results show that this method reduces the instances of print failure and improves the overall manipulability of the robot during printing.

Multi-robot systems possess the potential of becoming the next generation of robots in the mining industry due to their robustness and scalability. However, they present challenges for the system to efficiently allocate tasks to each robot and allow them to navigate toward their targets safely. This paper introduces a hybrid approach method for a multi-robot system, alongside with a case study in drilling and blasting automation. A Centralized Control Unit delegates tasks and information among the robots in the system, each equipped with a decentralized motion planner that supports cooperative inter- robot collision avoidance. The proposed system inherits the advantage of a centralized multi-robot system in providing a time-wise optimal solution; while also possessing the computational benefit and scalability of a decentralized system. Simulations were conducted to validate the proposed method and discuss insights into the efficacy and performance of the proposed method.

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.

It is becoming increasingly achievable for steel bridge structures, which are normally both inaccessible and hazardous for humans, to be inspected and maintained by autonomous robots. Steel bridges have been traditionally constructed by securing plate members together with rivets. However, rivets present a challenge for robots both in terms of cleaning and surface traversal. This paper presents a novel approach to RGB-D image and point cloud analysis that enables rivets to be rapidly and robustly located using low cost, non-contact sensing devices that can be easily affixed to a robot. The approach performs classification based on: (a) high-intensity blobs in color images, (b) the non-linear perturbations in depth images, and (c) surface normal clusters in 3D point clouds. The predicted rivet locations from the three classifiers are combined using a probabilistic occupancy mapping technique. Experiments are conducted in several different lab and real-world steel bridge environments, where there is no external lighting infrastructure, and the sensors are attached to a mobile platform, i.e. a climbing inspection robot. The location of rivets within 2m of the robot can be robustly located within 10mm of their correct location. The state of voxels can be predicted with above 95% accuracy, in approximately 1 second per frame.

Uncertainties in base placements of mobile, autonomous industrial robots can cause incomplete coverage in tasks such as grit-blasting and spray painting. Sensing and localization errors can cause such uncertainties in robot base placements. This paper addresses the problem of collaborative complete coverage under uncertainties through appropriate base placements of multiple mobile and autonomous industrial robots while aiming to optimize the performance of the robot team. A mathematical model for complete coverage under uncertainties is proposed and then solved using a stochastic multi-objective optimization algorithm. The approach aims to concurrently find an optimal number and sequence of base placements for each robot such that the robot team's objectives are optimized whilst uncertainties are accounted for. Several case studies based on a real-world application using a real-world object and a complex simulated object are provided to demonstrate the effectiveness of the approach for different conditions and scenarios, e.g. various levels of uncertainties, different numbers of robots, and robots with different capabilities.

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.

There are many benefits for the deployment of multiple autonomous industrial robots to carry out a task, particularly if the robots act in a highly collaborative manner. Collaboration can be possible when each robot is able to autonomously explore the environment, localize itself, create a map of the environment and communicate with other robots. This paper presents an approach to the modeling of the collaboration problem of multiple robots determining optimal base positions and orientations in an environment by considering the team objectives and the information shared amongst the robots. It is assumed that the robots can communicate so as to share information on the environment, their operation status and their capabilities. The approach has been applied to a team of robots that are required to perform complete surface coverage tasks such as grit-blasting and spray painting in unstructured environments. Case studies of such applications are presented to demonstrate the effectiveness of the approach.

This paper proposes an approach to improve surface-type classification of images containing inconsistently illuminated surfaces. When a mobile inspection robot is visually inspecting surface-types in a dark environment and a directional light source is used to illuminate the surfaces, the images captured may exhibit illumination variance that can be caused by the orientation and distance of the light source relative to the surfaces. In order to accurately classify the surface-types in these images, either the training image dataset needs to completely incorporate the illumination variance or a way to extract color features that can provide high classification accuracy needs to be identified. In this paper diffused reflectance values are extracted as new color features to classifying surface-types. In this approach, Red, Green, Blue-Depth (RGB-D) data is collected from the environment, and a reflectance model is used to calculate a diffused reflectance value for a pixel in each Red, Green, Blue (RGB) color channel. The diffused reflectance values can be used to train a multiclass support vector machine classifier to classify surface-types. Experiments are conducted in a mock bridge maintenance environment using a portable RGB-Depth sensor package with an attached light source to collect surface-type data. The performance of a classifier trained with diffused reflectance values is compared against classifiers trained with other color features including RGB and L*a*b* color spaces. Results show that the classifier trained with the diffused reflectance values can achieve consistently higher classification accuracy than the classifiers trained with RGB and L*a*b* features. For test images containing a single surface plane, diffused reflectance values consistently provide greater than 90% classification accuracy; and for test images containing a complex scene with multiple surface-types and surface planes, diffused reflectance values are shown to provide an increase in overall accuracy over RGB and L*a*b* by 49.24% and 13.66%, respectively.

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.