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1. Adaptive Multiple Distributed Bidirectional Spiral Path Planning for Foraging Robot Swarms

   https://doi.org/10.1109/CRV60082.2023.00036  

   Lu, Qi; Luna, Ryan (June 2023, IEEE)

   The Distributed Deterministic Spiral Algorithm (DDSA) has shown great foraging efficiency in robot swarms. However, when the number of robots in the swarm increases, scalability becomes a significant bottleneck due to increased collisions among robots, making it challenging to deploy them in the search space (e.g., 20 robots). To address this issue, we propose an adaptive Multiple-Distributed Bidirectional Spiral Algorithm (MDBSA) that enhances scalability. Our proposed algorithm partitions the squared search arena into multiple identical squared regions and assigns robots to regions dynamically based on the number of regions. In each region, a bidirectional spiral search path is planned, and when a robot completes its search, it is assigned to either an unassigned region or a region with one robot. The two robots will then travel along the path from the starting and ending points of the spiral path. We evaluated the performance of robot swarms using the MDBSA algorithm in the ARGoS robot simulator. Our experimental results show that the proposed MDBSA algorithm outperforms DDSA. When robots deliver collected resources to regions instead of the center, it reduces collisions and significantly improves the scalability of the robot swarm. Our findings suggest that a multiple-distributed search strategy is an efficient solution for foraging robot swarms. 

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2. Human-Robot Interaction with Drones and Drone Swarms in Law Enforcement Clearing Operations

   https://doi.org/10.1177/1071181319631465  

   Stone, Richard T.; Schnieders, Thomas M.; Push, Kevin A.; Terry, Stephen; Truong, Mary; Seshie, Inshira; Socha, Kathryn (November 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Police officers often must work alone in clearing operations, a procedure that involves surveying a building for threats and appropriately responding. A partnership between drone swarms and officers has potential to increase the safety of officers and civilians during these high-stress operations and reduce the risk of harm from hostile persons. This two part study examines aspects of trust, situational awareness, mental demand, performance, and human-robot interaction during law enforcement building clearing operations using either a single drone or a drone swarm. Results indicate that single drone use can increase time for operation, but accuracy and safety of clearing is enhanced. Single drone use saw increased situational awareness, a decrease in number of targets missed, and a moderate level of trust. For drone swarms, results indicate significant differences in mental workload from swarm data feeds compared to single drone feeds but no substantial difference in accuracy of finding targets. 

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3. Swarm Localization Through Cooperative Landmark Identification

   https://doi.org/10.1007/978-3-030-92790-5_33  

   Brent, Sarah; Yuan, Chengzhi; Stegagno, Paolo. (January 2022, International Symposium Distributed Autonomous Robotic Systems)

   In this paper we propose a landmark-based map localization system for robotic swarms. The proposed system leverages the capabilities of a distributed landmark identification algorithm developed for robotic swarms presented in [1]. The output of the landmark identification consists of a vector of probabilities that each individual robot is looking at a particular landmark in the environment. In this work, this vector is used individually by each component of the swarm to feed the measurement update of a particle filter to estimate the robot location. The system was tested in simulation to validate its performance. 

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4. Density-Driven Multi-Agent Swarm Control with Potential Field Based Collision Avoidance

   https://doi.org/10.1109/UEMCON59035.2023.10316140  

   Seo, Sungjun; Lee, Kooktae (October 2023, IEEE)

   In this paper, a density-driven multi-agent swarm control problem is investigated. Robot swarms can provide a great benefit, especially for applications where a single robot cannot effectively achieve a given task. For large spatial-scale applications such as search and rescue, environmental monitoring, and surveillance, a new multi-agent swarm control strategy is necessary because of physical constraints including a robot number and operation time. This paper provides a novel density-driven swarm control strategy for multi-agent systems based on the Optimal Transport theory, to cover a spacious domain with limited resources. In such a scenario, \textit{efficiency} will likely be a key point in achieving an efficient robot swarm behavior rather than uniform coverage that might be infeasible. With the given reference density, pre-constructed from available information, the proposed swarm control method will drive the multi-agent system such that their time-averaged behavior becomes similar to the reference density. In this way, density-driven swarm control will enable the multiple agents to spend most of their time on high-priority regions that are reflected in the reference density, leading to efficiency. To protect the agents from collisions, the Artificial Potential Field method is employed and combined with the proposed density-driven swarm control scheme. Simulations are conducted to validate density-driven swarm control as well as to test collision avoidance. Also, the swarm performance is analyzed by varying the agent number in the simulation. 

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5. Area-Optimized UAV Swarm Network for Search and Rescue Operations

   https://doi.org/10.1109/CCWC47524.2020.9031197  

   Ruetten, Laik; Regis, Paulo Alexandre; Feil-Seifer, David; Sengupta, Shamik (January 2020, Computing and Communication Workshop and Conference (CCWC))

   null (Ed.)

   Intelligent robot swarms are increasingly being explored as tools for search and rescue missions. Efficient path planning and robust communication networks are critical elements of completing missions. The focus of this research is to give unmanned aerial vehicles (UAVs) the ability to self-organize a mesh network that is optimized for area coverage. The UAVs will be able to read the communication strength between themselves and all the UAVs it is connected to using RSSI. The UAVs should be able to adjust their positioning closer to other UAVs if RSSI is below a threshold, and they should also maintain communication as a group if they move together along a search path. Our approach was to use Genetic Algorithms in a simulated environment to achieve multi-node exploration with emphasis on connectivity and swarm spread. 

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