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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)

   null (Ed.)

   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. 

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2. Search and Rescue Operations with Mesh Networked Robots

   Brodeur, Tristan; Regis, Paulo; Feil-Seifer, David; Sengupta, Shamik (November 2018, Proceedings - International Conference on Computer Communications and Networks)

   Efficient path planning and communication of multi-robot systems in the case of a search and rescue operation is a critical issue facing robotics disaster relief efforts. Ensuring all the nodes of a specialized robotic search team are within range, while also covering as much area as possible to guarantee efficient response time, is the goal of this paper. We propose a specialized search-and-rescue model based on a mesh network topology of aerial and ground robots. The proposed model is based on several methods. First, each robot determines its position relative to other robots within the system, using RSSI. Packets are then communicated to other robots in the system detailing important information regarding robot system status, status of the mission, and identification number. The results demonstrate the ability to determine multi-robot navigation with RSSI, allowing low computation costs and increased search-and-rescue time efficiency. 

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3. Measuring Human-Robot Team Benefits Under Time Pressure in a Virtual Reality Testbed

   Katarina Popovic, Millicent Schlafly (October 2023, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   During a natural disaster such as hurricane, earth- quake, or fire, robots have the potential to explore vast areas and provide valuable aid in search & rescue efforts. These scenar- ios are often high-pressure and time-critical with dynamically- changing task goals. One limitation to these large scale deploy- ments is effective human-robot interaction. Prior work shows that collaboration between one human and one robot benefits from shared control. Here we evaluate the efficacy of shared control for human-swarm teaming in an immersive virtual reality environment. Although there are many human-swarm interaction paradigms, few are evaluated in high-pressure settings representative of their intended end use. We have developed an open-source virtual reality testbed for realistic evaluation of human-swarm teaming performance under pressure. We conduct a user study (n=16) comparing four human-swarm paradigms to a baseline condition with no robotic assistance. Shared control significantly reduces the number of instructions needed to operate the robots. While shared control leads to marginally improved team performance in experienced participants, novices perform best when the robots are fully autonomous. Our experimental results suggest that in immersive, high-pressure settings, the benefits of robotic assistance may depend on how the human and robots interact and the human operator’s expertise. 

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4. Blockchain-Based Mechanism for Robotic Cooperation Through Incentives: Prototype Application in Warehouse Automation

   https://doi.org/10.1109/Blockchain53845.2021.00090  

   Grey, Jonathan; Seneviratne, Oshani; Godage, Isuru (December 2021, IEEE International Conference on Blockchain (Blockchain))

   The use of blockchain in cyber-physical systems, such as robotics, is an area with immense potential to address many shortcomings in robotic coordination and control. In traditional swarm robotic applications, where homogeneous robots are utilized, it is possible to replace a robot if it malfunctions, and it can be assumed that all robots are interchangeable. However, in many real-world applications spanning from search and rescue missions to future household robotic appliances, heterogeneous robots will need to work together with the other robots and human agents to achieve specific tasks. Nevertheless, no such system exists. Therefore, we propose a system that utilizes a token economy for robotic agents that makes agents responsive to token acquisition as an incentive for collaboration in achieving a given task. The economy enables the system to self-govern, even under Byzantine and adversarial settings. We further incorporate a novel subcontracting framework within a blockchain environment to allow the robotic agents to efficiently and cost-effectively perform complex jobs requiring multiple agents with various capabilities. We conducted a thorough evaluation of the system in a prototype warehouse application scenario, and the results are promising. 

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5. Towards computational awareness in autonomous robots: an empirical study of computational kernels

   https://doi.org/10.1007/s40747-023-01059-7  

   Sifat, Ashrarul H.; Bharmal, Burhanuddin; Zeng, Haibo; Huang, Jia-Bin; Jung, Changhee; Williams, Ryan K. (May 2023, Complex & Intelligent Systems)

   Abstract The potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must bescheduledandoptimizedto guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at runtime. In this paper, we consider a necessary first step towards this goal ofcomputational awarenessin autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot’s operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal. 

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