The Influence of Limited Visual Sensing on the Reynolds Flocking Algorithm

This paper presents the design of a decentralized connectivity-maintenance algorithm for the teleoperation of a team of multiple UAVs, together with an extensive human subject evaluation in virtual and real environments. The proposed connectivity-maintenance algorithm enhances earlier works by improving their applicability, safety, effectiveness, and ease of use, by including: (i) an airflow-avoidance behavior that avoids stack downwash phenomena in rotor-based aerial robots; (ii) a consensus-based action for enabling fast displacements with minimal topology changes by having all follower robots moving at the leader's velocity; (iii) an automatic decrease of the minimum degree of connectivity, enabling an intuitive and dynamic expansion/compression of the formation; and (iv) an automatic detection and resolution of deadlock configurations, i.e., when the robot leader cannot move due to counterbalancing connectivityand external-related inputs. We also devised and evaluated different interfaces for teleoperating the team as well as different ways of receiving information about the connectivity force acting on the leader. Results of two human subject experiments show that the proposed algorithm is effective in various situations. Moreover, using haptic feedback to provide information about the team connectivity outperforms providing both no feedback at all and sensory substitution via visual feedback. Note to Practitioners-The control of one drone is usually performed with a remote controller (similar to a joypad) that uses radio-wave signals. When controlling more than one drone, even a simple task such as moving the whole team around become very challenging. Developing an easy, yet efficient way to impart commands to a formation of drones is necessary to achieve any complex task. This work proposes a framework to control a fleet of drones (quadrotors) in an intuitive way, while receiving meaningful and effective information on the state of the formation. The proposed technique does not rely on any absolute positioning system (e.g., GPS) or centralized command center. Instead, it only uses the relative position of the drones with respect to each other, and all computations are designed in a decentralized fashion. These features make the proposed framework ready for deployment in different highimpact applications, such as in surveillance, search-and-rescue, and disaster response scenarios. Index Terms-Multi-robot systems, Human-centered robotics.

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“…. This way of modeling the robot formation is quite common in the field [4], [5], [34]. The environment is modeled as a collection of…”

Section: And Double Integrator Dynamicsmentioning

confidence: 99%

Connectivity-Maintenance Teleoperation of a UAV Fleet With Wearable Haptic Feedback

Aggravi

Pacchierotti²,

Giordano³

2021

IEEE Trans. Automat. Sci. Eng.

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“…In these situations, the swarm may split into multiple subgroups with no influence on one another, and collisions may occur. To evaluate the collective navigation performance during flight, we use five metrics adopted in previous work [31]. These metrics were inspired by robotic and biological studies of aerial swarms:…”

Section: F Performance Analysismentioning

confidence: 99%

SwarmLab: a Matlab Drone Swarm Simulator

Soria

Schiano

Floreano

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Among the available solutions for drone swarm simulations, we identified a lack of simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis. Moreover, users who want to dive in the research field of drone swarms often need to interface with multiple programming languages. We present SwarmLab, a software entirely written in MATLAB, that aims at the creation of standardized processes and metrics to quantify the performance and robustness of swarm algorithms, and in particular, it focuses on drones. We showcase the functionalities of SwarmLab by comparing two decentralized algorithms from the state of the art for the navigation of aerial swarms in cluttered environments, Olfati-Saber's and Vasarhelyi's. We analyze the variability of the inter-agent distances and agents' speeds during flight. We also study some of the performance metrics presented, i.e. order, inter-and extra-agent safety, union, and connectivity. While Olfati-Saber's approach results in a faster crossing of the obstacle field, Vasarhelyi's approach allows the agents to fly smoother trajectories, without oscillations. We believe that SwarmLab is relevant for both the biological and robotics research communities, and for education, since it allows fast algorithm development, the automatic collection of simulated data, the systematic analysis of swarming behaviors with performance metrics inherited from the state of the art.

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“…We therefore only consider agents as neighbors if they are closer than the desired cutoff distance r max which corresponds to only selecting agents in a sphere with a given radius. We do not make any restrictions on the field of view of the agents since limiting perception, specifically in the lateral direction, has been shown to have adverse effects on the flocking performance [22]. Therefore, we denote the set of neighbors of an agent i as the set N i = {agents j : j = i ∧ r ij < r max } where r ij ∈ R 3 denotes the relative position of agent j with respect to agent i and · the Euclidean norm.…”

Section: A Flocking Algorithmmentioning

confidence: 99%

Learning Vision-Based Flight in Drone Swarms by Imitation

Schilling

Lecoeur

Schiano

et al. 2019

IEEE Robot. Autom. Lett.

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Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This work proposes an entirely visual approach to coordinate markerless drone swarms based on imitation learning. Each agent is controlled by a small and efficient convolutional neural network that takes raw omnidirectional images as inputs and predicts 3D velocity commands that match those computed by a flocking algorithm. We start training in simulation and propose a simple yet effective unsupervised domain adaptation approach to transfer the learned controller to the real world. We further train the controller with data collected in our motion capture hall. We show that the convolutional neural network trained on the visual inputs of the drone can learn not only robust inter-agent collision avoidance but also cohesion of the swarm in a sample-efficient manner. The neural controller effectively learns to localize other agents in the visual input, which we show by visualizing the regions with the most influence on the motion of an agent. We remove the dependence on sharing positions among swarm members by taking only local visual information into account for control. Our work can therefore be seen as the first step towards a fully decentralized, vision-based swarm without the need for communication or visual markers.

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The Influence of Limited Visual Sensing on the Reynolds Flocking Algorithm

Cited by 26 publications

References 37 publications

Connectivity-Maintenance Teleoperation of a UAV Fleet With Wearable Haptic Feedback

Connectivity-Maintenance Teleoperation of a UAV Fleet With Wearable Haptic Feedback

SwarmLab: a Matlab Drone Swarm Simulator

Learning Vision-Based Flight in Drone Swarms by Imitation

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