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1. AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training

   https://doi.org/10.3390/s20185223  

   Chen, Junjie ; Li, Shuai ; Liu, Donghai ; Li, Xueping ( September 2020 , Sensors)

   Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.
2. Distributed Multi-Target Search and Tracking Using a Coordinated Team of Ground and Aerial Robots

   Chen, Jun ; Dames, Philip ( July 2020 , Robotics science and systems)

   Presented at the Workshop on Heterogeneous Multi-Robot Task Allocation and Coordination. The authors recently developed a distributed algorithm to enable a team of homogeneous robots to search for and track an unknown and time-varying number of dynamic targets. This algorithm combined a distributed version of the PHD filter (for multi-target tracking) with Lloyd’s algorithm to drive the motion of the robots. In this paper we extend this previous work to allow a heterogeneous team of groundand aerial robots to perform the search and tracking tasks in a coordinated manner. Both types of robots are equipped with sensors that have a finite field of view and which may receive both false positive and false negative detections. Theaerial robots may vary the size of their sensor field of view (FoV) by changing elevation. This increase in the FoV coincides with a decrease in the accuracy and reliability of the sensor. The ground robots maintain the target tracking information while the aerial robots provide additional sensor coverage. We develop two new distributed algorithms to provide filter updates and to make control decisions in this heterogeneous team. Both algorithms only require robots to communicate with nearby robots and use minimal bandwidth.We demonstrate the efficacymore »of our approach through a series of simulated experiments which show that the heterogeneous teams are able to achieve more accurate tracking in less time than our previous work.« less
3. Impact of Immersive Training on Senior Chemical Engineering Students' Prioritization of Process Safety Decision Criteria

   Stransky, J. ( July 2021 , ASEE Annual Conference proceedings)

   Process safety is becoming a greater focus of chemical plant design and operation due to the number of incidents involving dangerous chemical accidents. Since its creation nearly 20 years ago, the Chemical Safety Board (CSB) has investigated 130 safety incidents and provided over 800 safety recommendations to operating chemical facilities. Following a gas well blowout in 2018, the CSB gave a recommendation to the American Petroleum Institute (API) to establish recommended practice on alarm management. Similarly, in 2017, the CSB gave a recommendation to Arkema Inc. to update their emergency response training following a hurricane that caused a fire at one of their manufacturing sites. Many times, CSB-led investigations resulted in new regulations and standards that are enforced by the Occupational Safety and Health Administration (OSHA) or the Environmental Protection Agency (EPA). These critical recommendations positively impact not only the plant workers but also the surrounding community and the environment. While these safety measures enhance industrial safety culture, it is important that process safety also be integrated into university-level engineering curricula to promote safety culture while future engineers are still developing. Integrating process safety into the curriculum prepares students by familiarizing them with the difficult decisions they will be requiredmore »to make in professional practice. ABET, the engineering program accreditation body, acknowledges the value of early, appropriate training within their program guidelines “Criteria for Chemical Engineering Curriculum” which states that recognition and assessment of the hazards associated with chemical processes must be included in the curriculum for program accreditation. Based on this requirement, many institutions have taken the approach to integrate process safety into their curriculum using video case studies, adding entire courses to cover hazard identification, and including safety lectures in design courses. A common theme missing from these methods is instruction on how to approach, recognize, and navigate decisions within a process safety context; a lack of this situational awareness was noted as a key element in industrial process safety incidents. Understanding how students approach process safety decisions is important for developing teaching methods and curriculum that will better prepare them for professional practice. As part of this study, we will measure how students rank criteria associated with process safety decisions, and how these prioritizations change after exposure to a process safety decision making intervention. Through this work, we hope to determine how process safety curriculum may be improved to help better prepare students for process safety decisions within industry.« less
4. Empirical Characterization of a High-performance Exterior-rotor Type Brushless DC Motor and Drive

   https://doi.org/10.1109/IROS40897.2019.8967626  

   Lee, Ung Hee ; Pan, Chen-Wen ; Rouse, Elliott J. ( November 2019 , Proceedings of the IEEE Conference of Intelligent Robots and Systems)

   Recently, brushless motors with especially high torque densities have been developed for applications in autonomous aerial vehicles (i.e. drones), which usually employ exterior rotortype geometries (ER-BLDC motors). These motors are promising for other applications, such as humanoids and wearable robots; however, the emerging companies that produce motors for drone applications do not typically provide adequate technical specifications that would permit their general use across robotics-for example, the specifications are often tested in unrealistic forced convection environments, or are drone-specific, such as thrust efficiency. Furthermore, the high magnetic pole count in many ER-BLDC motors restricts the brushless drives able to efficiently commutate these motors at speeds needed for lightly-geared operation. This paper provides an empirical characterization of a popular ER-BLDC motor and a new brushless drive, which includes efficiencies of the motor across different power regimes, identification of the motor transfer function coefficients, thermal response properties, and closed loop control performance in the time and frequency domains. The intent of this work is to serve as a benchmark and reference for other researchers seeking to utilize these exciting and emerging motor geometries.
5. Explaining Autonomous Decisions in Swarms of Human-on-the-Loop Small Unmanned Aerial Systems

   Ankit Agrawal ; Jane Cleland-Huang ( October 2021 , Proceedings the AAAI Conference on Human Computation and Crowdsourcing)

   Rapid advancements in Artificial Intelligence have shifted the focus from traditional human-directed robots to fully autonomous ones that do not require explicit human control. These are commonly referred to as Human-on-the-Loop (HotL) systems. Transparency of HotL systems necessitates clear explanations of autonomous behavior so that humans are aware of what is happening in the environment and can understand why robots behave in a certain way. However, in complex multi-robot environments, especially those in which the robots are autonomous and mobile, humans may struggle to maintain situational awareness. Presenting humans with rich explanations of autonomous behavior tends to overload them with lots of information and negatively affect their understanding of the situation. Therefore, explaining the autonomous behavior of multiple robots creates a design tension that demands careful investigation. This paper examines the User Interface (UI) design trade-offs associated with providing timely and detailed explanations of autonomous behavior for swarms of small Unmanned Aerial Systems (sUAS) or drones. We analyze the impact of UI design choices on human awareness of the situation. We conducted multiple user studies with both inexperienced and expert sUAS operators to present our design solution and initial guidelines for designing the HotL multi-sUAS interface.