Low-cost embedded system for relative localization in robotic swarms

Faigl, Jan; Krajník, Tomáš; Chudoba, J.; Přeučil, Libor; Saska, Martin

doi:10.1109/icra.2013.6630694

Cited by 86 publications

(61 citation statements)

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“…For details and experiments identifying the sensor model, see [38], [36]. Except the viewing angle, which can be clearly defined for each optical system (based on the lens), the most important factors that need to be considered in swarm stabilization and motion planning are the measurement accuracy and reliability.…”

Section: A Relative Localization System Performancementioning

confidence: 99%

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System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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Abstract-A complex system for control of swarms of Micro Aerial Vehicles (MAV), in literature also called as Unmanned Aerial Vehicles (UAV) or Unmanned Aerial Systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visualbased stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

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Section: A Relative Localization System Performancementioning

confidence: 99%

“…18, are considered in the workspace to demonstrate the obstacle avoidance ability. Three MAVs equipped with the visual relative localization modules [38] are stabilized in triangular and line formations. In the tri- angular formation (see Fig.…”

Section: Multi-robot Scenarios Demonstrating the Practical Usabilmentioning

confidence: 99%

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

et al. 2016

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“…The idea of the proposed pattern detection has been originally motivated by practical needs for a relative localization of small unmanned aerial vehicles (UAV) [17] to keep them in a swarm formation where a fast image processing using only on-board computational resources is required. Considering the previous passive localization approaches, we found out that the proposed roundel detector best fits our needs for fast estimation of a relative distance of flying UAVs using only the on-board computational resources.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we consider the principle of the roundel detection [17] in a global localization scenario, where the method is able to detect and track several thousands of images per second on a common desktop computer. Moreover, we propose a model of the localization arising from theoretical analyses of the vision system and experimental evaluation of the system performance in real scenarios with regard to its practical deployment.…”

Section: Introductionmentioning

confidence: 99%

“…The system has been already deployed in a number of international mobile robotics projects concerning distributed quad rotor localization [17], visual based autonomous navigation [18], decentralized formation control [19], evolutionary swarm [20] and educational [15] robotics. Since the system has already proved to be useful in a variety of applications, we publish its source code [21]; so, other roboteers can use it for their projects.…”

Section: Introductionmentioning

confidence: 99%

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External localization system for mobile robotics

Krajník

Nitsche

Faigl

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

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Abstract-We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems.

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Distributed cohesive configuration controller for a swarm with low‐cost platforms

Lee

2022

Journal of Field Robotics

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This study presents a cohesive configuration controller for distributed space coverage by a swarm of robots. The goal is to build a dense, convex network that is robust against disconnection while robots are flocking with only incomplete knowledge about the network. The controller is an integrated framework of two different algorithms. First, we present a boundary force algorithm: physics‐based swarm intelligence that borrows the concept of surface tension force between liquid molecules. The combination of such a force with conventional flocking produces a convex and dense configuration without knowledge of the complete geometry of a robot network. Second, robots distributively determine when a configuration is on the verge of disconnection by identifying a local articulation point—a region where the removal of a single robot will change the local topology. When such a point is detected, robots switch their behavior to clustering, which aggregates them around the vulnerable region to remove every articulation point and retain a connected configuration. Finally, we introduced an index that objectively represents the level of risk of a robot configuration against the massive fragmentation, called vulnerability index. We provide theoretical performance analyses of each algorithm and validate the results with simulations and experiments using a set of low‐cost robots.

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Low-cost embedded system for relative localization in robotic swarms

Cited by 86 publications

References 10 publications

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

External localization system for mobile robotics

Distributed cohesive configuration controller for a swarm with low‐cost platforms

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