Autonomous Drone Racing: A Survey

Hanover, Drew; Loquercio, Antonio; Bauersfeld, Leonard; Romero, Angel; Pěnička, Robert; Song, Yi; Cioffi, Giovanni; Kaufmann, Elia; Scaramuzza, Davide

doi:10.48550/arxiv.2301.01755

Cited by 3 publications

(4 citation statements)

References 141 publications

(238 reference statements)

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“…The pixel-wise intensity changes, called events or spikes, are recorded at a temporal resolution on the order of microseconds. Event-based cameras have been applied in autonomous drone racing [38], space imaging [39], space exploration [40], automated drilling [41], and visual servoing [42,43]. Neuromorphic cameras' fast update rate, along with their high dynamic range (140 dB compared to conventional cameras with 60 dB [44]) and low power consumption, make them apt for robotics tasks [45].…”

Section: Neuromorphic Vision-based Tactile Sensingmentioning

confidence: 99%

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Sajwani¹,

Ayyad²,

Alkendi³

et al. 2023

Preprint

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Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.

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Section: Neuromorphic Vision-based Tactile Sensingmentioning

confidence: 99%

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Sajwani¹,

Ayyad²,

Alkendi³

et al. 2023

Preprint

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show abstract

Section: Reinforcement Learningmentioning

confidence: 99%

“…The RL can update the knowledge of agents through trial and exploration worldwide (a common structure is shown in Figure 4) without a supervisor. Driven by this characteristic, it may potentially surpass human expert performance [30]. There are many variations in RL and value-based methods, including Q-learning, deep Q-network, policy-based deterministic policy gradient, trust region policy optimization [31], and proximal policy optimization (PPO) [32].…”

Section: Reinforcement Learningmentioning

confidence: 99%

Model-Free Guidance Method for Drones in Complex Environments Using Direct Policy Exploration and Optimization

Liu

Suzuki

2023

Drones

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In the past few decades, drones have become lighter, with longer hang times, and exhibit more agile performance. To maximize their capabilities during flights in complex environments, researchers have proposed various model-based perception, planning, and control methods aimed at decomposing the problem into modules and collaboratively accomplishing the task in a sequential manner. However, in practical environments, it is extremely difficult to model both the drones and their environments, with very few existing model-based methods. In this study, we propose a novel model-free reinforcement-learning-based method that can learn the optimal planning and control policy from experienced flight data. During the training phase, the policy considers the complete state of the drones and environmental information as inputs. It then self-optimizes based on a predefined reward function. In practical implementations, the policy takes inputs from onboard and external sensors and outputs optimal control commands to low-level velocity controllers in an end-to-end manner. By capitalizing on this property, the planning and control policy can be improved without the need for an accurate system model and can drive drones to traverse complex environments at high speeds. The policy was trained and tested in a simulator, as well as in real-world flight experiments, demonstrating its practical applicability. The results show that this model-free method can learn to fly effectively and that it holds great potential to handle different tasks and environments.

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“…The pixel-wise intensity changes, called events or spikes, are recorded at a temporal resolution on the order of microseconds. Event-based cameras have been applied in autonomous drone racing [39], space imaging [40], space exploration [41], automated drilling [42], and visual servoing [43,44]. Neuromorphic cameras' fast update rate, along with their high dynamic range (140 dB compared to conventional cameras with 60 dB [45]) and low power consumption, make them apt for robotics tasks [46].…”

Section: Neuromorphic Vision-based Tactile Sensingmentioning

confidence: 99%

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Sajwani

Ayyad

Alkendi

et al. 2023

Sensors

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Vision-based tactile sensors (VBTSs) have become the de facto method for giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTSs offer high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead.In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. In particular, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTSs use an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios (with and without an internal illumination source) thus further reducing instrumentation costs. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the computing time needed by VBTS when both are tested on the same scenario.

show abstract

Autonomous Drone Racing: A Survey

Cited by 3 publications

References 141 publications

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

Model-Free Guidance Method for Drones in Complex Environments Using Direct Policy Exploration and Optimization

TactiGraph: An Asynchronous Graph Neural Network for Contact Angle Prediction Using Neuromorphic Vision-Based Tactile Sensing

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