A Survey of Robot Swarms’ Relative Localization Method

Chen, Siyuan; Yin, Dong; Niu, Yifeng

doi:10.3390/s22124424

Cited by 29 publications

(12 citation statements)

References 94 publications

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“…GPS signals can be disrupted or lost due to various factors such as signal jamming, multipath interference, and natural obstructions such as mountains, trees, and buildings. In such scenarios, swarms that heavily depend on GPS can face serious performance issues and may also suffer from complete failure [24]. To address this challenge, swarms must be designed to be more robust and resilient to GPS unavailability.…”

Section: Swarming In a Gps-denied Environmentmentioning

confidence: 99%

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SmrtSwarm: A Novel Swarming Model for Real-World Environments

Bhamu,

Verma,

Dixit

et al. 2023

Drones

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Drone swarms have gained a lot of popularity in recent times because, as a group, drones can perform highly intelligent tasks. Drone swarms are strongly inspired by the flocking behavior of birds, insects, and schools of fish, where all the members work in a coordinated manner to achieve a common goal. Since each drone is an independent entity, automating the control of a swarm is difficult. Previous works propose various swarming models with either centralized or distributed control. With distributed control, each drone makes its own decisions based on a small set of rules to accomplish swarm behavior, whereas in centralized control, one drone acts as the leader, who knows the final destination and the path to follow; it specifies the trajectories and velocities for the rest of the drones. Almost all the work in the area of swarming models follows Reynolds’ model, which has three basic rules. For GPS-aided settings, state-of-the-art proposals are not mature enough to handle complex environments with obstacles where primarily local decisions are taken. We propose a new set of rules and a game-theoretic method to set the values of the hyperparameters to design robust swarming algorithms for such scenarios. Similarly, the area of realistic swarming in GPS-denied environments is very sparse, and no work simultaneously handles obstacles and ensures that the drones stay in a confined zone and move along with the swarm. Our proposed solution SmrtSwarm solves all of these problems. It is the first comprehensive model that enables swarming in all kinds of decentralized environments regardless of GPS signal availability and obstacles. We achieve this by using a stereo camera and a novel algorithm that quickly identifies drones in depth maps and infers their velocities and identities with reference to itself. We implement our algorithms on the Unity gaming engine and study them using exhaustive simulations. We simulate 15-node swarms and observe cohesive swarming behavior without seeing any collisions or drones drifting apart. We also implement our algorithms on a Beaglebone Black board and show that even in a GPS-denied setting, we can sustain a frame rate of 75 FPS, much more than what is required in practical settings.

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Section: Swarming In a Gps-denied Environmentmentioning

confidence: 99%

“…The drones thus need distance-sensing hardware [22,24]. Distance sensors are typically limited in range and accuracy.…”

Section: Swarming In a Gps-denied Environmentmentioning

confidence: 99%

SmrtSwarm: A Novel Swarming Model for Real-World Environments

Bhamu,

Verma,

Dixit

et al. 2023

Drones

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“…In the paper different device-based, monitor-based, and proximity detection methods are compared based on localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS), and on technologies, such as Wi-Fi, RFID, UWB, and Bluetooth. A similar compilation is presented in [7], where the stateof-the-art relative localization methods for robot swarms are surveyed.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Visual-Inertial Localization of Teams With Floorplan Extraction

Gazdag,

Pasztornicky,

Jankó

et al. 2023

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)

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This paper showcases a real-world example of a system that achieves collaborative localization and mapping of multiple agents within a building. The proposed system processes the odometry and 3D point cloud data collected by the agents moving around the building to automatically generate the building's floorplan on which the agent trajectories are overlaid. The wearable hardware consists of a low-cost passive integrated sensor that includes both a camera and an IMU (Inertial Measurement Unit) and an embedded compute unit. The system's capabilities are shown through real-world experiments.

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“…These methods can be divided into three categories: inertial/dead reckoning, acoustic transponders and modems, and geophysical. Most recently, in [ 16 ], a robot swarm relative localization method was studied, and in [ 17 ], a review of localization, navigation, and communication of UUVs for collaborative missions is presented. In [ 18 ], the fusion of an inertial sensor and a vision is introduced.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Relative Localization Estimation in a Coordinated Formation Control of Low-Cost Underwater Drones

Soriano

Pham

Giès

2023

Sensors

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This study presents a relative localization estimation method for a group of low-cost underwater drones (l-UD), which only uses visual feedback provided by an on-board camera and IMU data. It aims to design a distributed controller for a group of robots to reach a specific shape. This controller is based on a leader–follower architecture. The main contribution is to determine the relative position between the l-UD without using digital communication and sonar positioning methods. In addition, the proposed implementation of the EKF to fuse the vision data and the IMU data improves the prediction capability in cases where the robot is out of view of the camera. This approach allows the study and testing of distributed control algorithms for low-cost underwater drones. Finally, three robot operating system (ROS) platform-based BlueROVs are used in an experiment in a near-realistic environment. The experimental validation of the approach has been obtained by investigating different scenarios.

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A Survey of Robot Swarms’ Relative Localization Method

Cited by 29 publications

References 94 publications

SmrtSwarm: A Novel Swarming Model for Real-World Environments

SmrtSwarm: A Novel Swarming Model for Real-World Environments

Collaborative Visual-Inertial Localization of Teams With Floorplan Extraction

Experimental Investigation of Relative Localization Estimation in a Coordinated Formation Control of Low-Cost Underwater Drones

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