Distance-Based Multi-Robot Coordination on Pocket Drones

Broecker, Bastian; Tuyls, Karl; Butterworth, James

doi:10.1109/icra.2018.8461176

Cited by 8 publications

(10 citation statements)

References 3 publications

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“…Moving into nano-scale UAVs, we focus on those which employ novel deep learning-based algorithms [14], [23], [24], [25]. These approaches are rapidly gaining attention, as they are lightweight compared to the more traditional ones based on the localization-mapping-planning cycle [8], and represent a natural fit within resource-constrained nano-drones.…”

Section: Related Workmentioning

confidence: 99%

“…Moving to nano-sized automatic systems, i.e., requiring off-board resources, the SoA is characterized by a broad spectrum of use-cases and solutions. For example, visionbased DL/RL algorithms for obstacle avoidance [24], [25], [26] being computed on external computers, and nano-drones localization systems with additional ad-hoc infrastructure (e.g., ultra-wideband anchors, motion-capture cameras, etc.) [24], [27].…”

Section: Related Workmentioning

confidence: 99%

“…For example, visionbased DL/RL algorithms for obstacle avoidance [24], [25], [26] being computed on external computers, and nano-drones localization systems with additional ad-hoc infrastructure (e.g., ultra-wideband anchors, motion-capture cameras, etc.) [24], [27]. Offloading computational-intense workloads to remote base-stations can enable complex algorithms fed with abundant sensory data streams from the aircraft.…”

Section: Related Workmentioning

confidence: 99%

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Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

Palossi

Zimmerman

Burrello

et al. 2022

IEEE Internet Things J.

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Many emerging applications of nano-sized unmanned aerial vehicles (UAVs), with a few cm 2 form-factor, revolve around safely interacting with humans in complex scenarios, for example, monitoring their activities or looking after people needing care. Such sophisticated autonomous functionality must be achieved while dealing with severe constraints in payload, battery, and power budget (∼100 mW). In this work, we attack a complex task going from perception to control: to estimate and maintain the nano-UAV's relative 3D pose with respect to a person while they freely move in the environment -a task that, to the best of our knowledge, has never previously been targeted with fully onboard computation on a nano-sized UAV. Our approach is centered around a novel vision-based deep neural network (DNN), called PULP-Frontnet, designed for deployment on top of a parallel ultra-low-power (PULP) processor aboard a nano-UAV. We present a vertically integrated approach starting from the DNN model design, training, and dataset augmentation down to 8-bit quantization and deployment in-field. PULP-Frontnet can operate in real-time (up to 135 frame/s), consuming less than 87 mW for processing at peak throughput and down to 0.43 mJ/frame in the most energy-efficient operating point. Field experiments demonstrate a closed-loop top-notch autonomous navigation capability, with a tiny 27-grams Crazyflie 2.1 nano-UAV. Compared against an ideal sensing setup, onboard pose inference yields excellent drone behavior in terms of median absolute errors, such as positional (onboard: 41 cm, ideal: 26 cm) and angular (onboard: 3.7 • , ideal: 4.1 • ). We publicly release videos and the source code of our work.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

Palossi

Zimmerman

Burrello

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

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“…The most sophisticated UAVs make use of LIDARs and special cameras that measure both odometry and speed. Ultra-Wide Band (UWB) transceivers are only used in one of the UAV systems listed in Table 2 [92], although they have been previously analyzed in the literature when performing accurate (centimeter-precision) distance measurements [100].…”

Section: Positioning Subsystemmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

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“…This sensor configuration removes the ability of the agent to know its relative orientation towards the target, therefore finding the target must be done by accumulating distance measurements and performing significant cognition in order to ascertain the direction in which to travel. This sensor configuration commonly occurs in robotics where a distance sensor -such as an Ultra-Wideband Frequency chip -provides just distance information with respect to other chips, as in the work of [23].…”

Section: No Bearing Sensormentioning

confidence: 99%

Evolving indoor navigational strategies using gated recurrent units in NEAT

Butterworth

Savani

Tuyls

2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Simultaneous Localisation and Mapping (SLAM) algorithms are expensive to run on smaller robotic platforms such as Micro-Aerial Vehicles. Bug algorithms are an alternative that use relatively little processing power, and avoid high memory consumption by not building an explicit map of the environment. Bug Algorithms achieve relatively good performance in simulated and robotic maze solving domains. However, because they are hand-designed, a natural question is whether they are globally optimal control policies. In this work we explore the performance of Neuroevolutionspecifically NEAT -at evolving control policies for simulated differential drive robots carrying out generalised maze navigation. We extend NEAT to include Gated Recurrent Units (GRUs) to help deal with long term dependencies. We show that both NEAT and our NEAT-GRU can repeatably generate controllers that outperform I-Bug (an algorithm particularly well-suited for use in real robots) on a test set of 209 indoor maze like environments. We show that NEAT-GRU is superior to NEAT in this task but also that out of the 2 systems, only NEAT-GRU can continuously evolve successful controllers for a much harder task in which no bearing information about the target is provided to the agent.

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Distance-Based Multi-Robot Coordination on Pocket Drones

Cited by 8 publications

References 3 publications

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

Evolving indoor navigational strategies using gated recurrent units in NEAT

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