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%.

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.

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.