Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunities

Wan, Zishen; Lele, Ashwin Sanjay; Bo, Yu; Liu, Shaoshan; Wang, Yu; Reddi, Vijay Janapa; Hao, Cong; Raychowdhury, Arijit

doi:10.1109/aicas54282.2022.9869951

Cited by 14 publications

(13 citation statements)

References 35 publications

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“…However, changing workloads on reconfiguring robotic computing at run-time during flight could lead to non-real-time performance. In addition, suffering from diverse hardware components and inefficient ROS support, better mapping aerial robotic computing on heterogeneous platforms poses a difficult but worthwhile challenge to overcome [4]. Thus, the next important topic is investigating hardware/software (HW/SW) co-design in FMS characterized by heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

“…At the path planning level, several approaches [156,157] are proposed to minimize the total energy consumption by UAVs for ML-added-well planned trajectory. At the task management level, Wan et al [4] presented a runtime-reconfigurable compute platform to enable aerial robots to be adaptive in diverse environments so that the task of battery management is highly dynamic for different workloads. At the coding level, converting the AI model into edge-deployment-friendly types, such as TensorFlow Lite [158], ONNX [159], and NCNN [160] could help.…”

Section: Discussionmentioning

confidence: 99%

“…Wan et al [4] reviewed the literature from the period and traversed down Figure 4 to explain robotic-specific computing. The general robot design algorithms can be divided into perception, localization and mapping, decision-making and control, Artificial Intelligence (AI), and cloud computing.…”

Section: Abstraction Layermentioning

confidence: 99%

“…Enabling the next generation of UAVs to process data from richer sensors and actuators and maximizing the performance with large-scale probabilistic ML models bears enormous potential [3]. However, the continuous proliferation of drones pose computing challenges, augmented by the higher performance necessities, big data-processing requirements, resource constraints, dynamic managing scenarios, and cyber-security concerns [4]. The greater the sophistication of the UAV, the greater the probability that a sub-system will fail [5].…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang

et al. 2023

Drones

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In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Abstraction Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang

et al. 2023

Drones

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“…In order for robotic systems to operate safely and effective in dynamic real-world environments, their computations must run at real-time rates while meeting power constraints. Towards this end, accelerating robotic kernels on heterogeneous hardware, such as GPUs and FPGAs, is emerging as a crucial tool for enabling such performance [1], [2], [3], [4], [5], [6], [7]. This is particularly important given the impending end of Moore's Law and the end of Dennard Scaling, which limits single CPU performance [8], [9].…”

Section: Introductionmentioning

confidence: 99%

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Mayoral-Vilches¹,

Neuman

Plancher

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We introduce RobotPerf, a vendor-agnostic benchmarking suite designed to evaluate robotics computing performance across a diverse range of hardware platforms using ROS 2 as its common baseline. The suite encompasses ROS 2 packages covering the full robotics pipeline and integrates two distinct benchmarking approaches: black-box testing, which measures performance by eliminating upper layers and replacing them with a test application, and grey-box testing, an application-specific measure that observes internal system states with minimal interference. Our benchmarking framework provides ready-to-use tools and is easily adaptable for the assessment of custom ROS 2 computational graphs. Drawing from the knowledge of leading robot architects and system architecture experts, RobotPerf establishes a standardized approach to robotics benchmarking. As an open-source initiative, RobotPerf remains committed to evolving with community input to advance the future of hardware-accelerated robotics.

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A co-operative type of multi-robot parking system with versatile mode and implementation using FPGA

Vani,

Karumuri,

Chinnaiah

et al. 2023

Microsyst Technol

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Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunities

Cited by 14 publications

References 35 publications

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

A co-operative type of multi-robot parking system with versatile mode and implementation using FPGA

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