A Multirobot System for Distributed Area Coverage and Signal Searching in Large Outdoor Scenarios*

Garzón, Mario; Valente, João; Roldán, Juan Jesús; Cancar, Leandro; Barrientos, Antonio; Cerro, Jaime del

doi:10.1002/rob.21636

Cited by 25 publications

(25 citation statements)

References 14 publications

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“…The management of multi-robots could be applied in harsh environments, such as mountains and valleys, which contain many different kinds of obstacles [5][6][7][8]. On these difficult fields, some properties of swarms allow them to adapt their behavior to face situations like damage of one of their robots.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, external factors must be taken into account to provide a safety zone around the group of migrants. With respect to this challenge, some studies have been conducted regarding a mobile group of moving rovers interacting with external factors that have an impact on the group's decision [5][6][7][8]. The suggested system should track the migrants, compute some optimal way-points around the migrants and manage the robot's faults.…”

Section: Introductionmentioning

confidence: 99%

“…We chose to detect four situations, one normal and three abnormal states: a normal move of a rover on stones, a fall of one rover, a collision with an obstacle, and a skid on sand. Methods of human-swarm interaction mostly rely on orders given by operators via different interfaces, like computers and smart watches [5][6][7][8]. The level of automation is reduced with such interface.…”

Section: Introductionmentioning

confidence: 99%

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Toward a robot swarm protecting a group of migrants

Vaidis

Otis

2020

Intel Serv Robotics

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Different geopolitical conflicts of recent years have led to mass migration of several civilian populations. These migrations take place in militarized zones, indicating real danger contexts for the populations. Indeed, civilians are increasingly targeted during military assaults. Defense and security needs have increased; therefore, there is a need to prioritize the protection of migrants. Very few or no arrangements are available to manage the scale of displacement and the protection of civilians during migration. In order to increase their security during mass migration in an inhospitable territory, this article proposes an assistive system using a team of mobile robots, labeled a rover swarm that is able to provide safety area around the migrants. We suggest a coordination algorithm including CNN and fuzzy logic that allows the swarm to synchronize their movements and provide better sensor coverage of the environment. Implementation is carried out using on a reduced scale rover to enable evaluation of the functionalities of the suggested software architecture and algorithms. Results bring new perspectives to helping and protecting migrants with a swarm that evolves in a complex and dynamic environment.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Toward a robot swarm protecting a group of migrants

Vaidis

Otis

2020

Intel Serv Robotics

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“…Although M is state-dependent, we may assume an additional constraint D(t) in Assumption 2 so that L(x(·), u, t) ≥ M(x(·), t) ≥ D(t). This imposes a timedependent convergence rate of the form J E x * i (·) −J E x * i−1 (·) ≤ − t i t i−1 D(t) dt in (20). An upper-bound on convergence rate is then the highest rate, determined by D(t), for which there always exists a single control action of finite duration that satisfies the constraint requirement, so that open-loop problem (8) attains a solution at each time step.…”

Section: B Convergence Ratementioning

confidence: 99%

Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

et al. 2018

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Abstract-Although a number of solutions exist for the problems of coverage, search and target localization-commonly addressed separately-whether there exists a unified strategy that addresses these objectives in a coherent manner without being application-specific remains a largely open research question. In this paper, we develop a receding-horizon ergodic control approach, based on hybrid systems theory, that has the potential to fill this gap. The nonlinear model predictive control algorithm plans real-time motions that optimally improve ergodicity with respect to a distribution defined by the expected information density across the sensing domain. We establish a theoretical framework for global stability guarantees with respect to a distribution. Moreover, the approach is distributable across multiple agents, so that each agent can independently compute its own control while sharing statistics of its coverage across a communication network. We demonstrate the method in both simulation and in experiment in the context of target localization, illustrating that the algorithm is independent of the number of targets being tracked and can be run in real-time on computationally limited hardware platforms.

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“…Garzon et al present a solution for multiple UGV to perform signal searching tasks in large outdoor scenarios. 24 They propose different path planning strategies for coverage, which depend on the size and shape of the field. Careful consideration of the aerial search patterns is important for extending our work.…”

Section: Unmanned Systems Collaborationmentioning

confidence: 99%

Radiation search operations using scene understanding with autonomous UAV and UGV

Christie

Shoemaker

Kochersberger

et al. 2017

Journal of Field Robotics

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Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data is used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g. road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.

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A Multirobot System for Distributed Area Coverage and Signal Searching in Large Outdoor Scenarios*

Cited by 25 publications

References 14 publications

Toward a robot swarm protecting a group of migrants

Toward a robot swarm protecting a group of migrants

Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

Radiation search operations using scene understanding with autonomous UAV and UGV

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