Waypoint Planning Networks

Toma, Alexandru-Iosif; Jaafar, Hussein Ali; Hsueh, Hao-Ya; James, Stephen; Lenton, Daniel; Clark, Ronald; Saeedi, Sajad

doi:10.1109/crv52889.2021.00020

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…On the other hand, sampling‐based methods are fast but suboptimal. Machine learning has been utilized in graph‐based searches to provide quick and optimal approaches to local planning to reduce computational time (Yonetani et al, 2021) and search space (Qureshi et al, 2019; Toma et al, 2021). Furthermore, value iteration networks provide differentiable path planning modules which can learn to plan (Lee et al, 2018; Tamar et al, 2017).…”

Section: Safe Transitmentioning

confidence: 99%

Autonomous aerial robotics for package delivery: A technical review

Saunders

Saeedi

2023

Journal of Field Robotics

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Small unmanned aerial vehicles (UAVs) have gained significant interest in the last decade. More specifically these vehicles have the capacity to impact package delivery logistics in a disruptive way. This paper reviews research problems and state‐of‐the‐art solutions that facilitate package delivery. Different aerial manipulators and grippers are listed along with control techniques to address stability issues. Landing on a platform is next discussed which encompasses static and dynamic platforms. Landing on a dynamic platform presents further challenges. This includes delayed control responses and poor precision of the relative motion between the platform and the aerial vehicle. Subsequently, risks such as weather conditions, state estimation, and collision avoidance to ensure safe transit is considered. Finally, delivery UAV routing is investigated which categorizes the topic into two areas: drone operations and drone–truck collaborative operations. Additionally, we compare the solutions against design, environmental, and legal constraints.

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Section: Safe Transitmentioning

confidence: 99%

Autonomous aerial robotics for package delivery: A technical review

Saunders

Saeedi

2023

Journal of Field Robotics

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“…• Learning-based path planning algorithms including Value Iteration Networks (VIN) [4], Motion Planning Networks (MPNet) [6], Gated Path Planning Networks (GPPN) [5], Online LSTM [7], CAE-LSTM [8], Bagging LSTM [9], and Waypoint Planning Networks (WPN) [9].…”

Section: Supported Path Planning Algorithmsmentioning

confidence: 99%

“…The platform provides interfaces for algorithm visualization, rapid development, training, training data generation and benchmarking analysis. Waypoint Planning Networks (WPN) [9] is an algorithm developed within PathBench to showcase its feasibility. • PathBench's benchmarking features allow evaluation against the suites of added path planning algorithms, both classical algorithms and machine-learned models, with standardized metrics and environments.…”

Section: Introductionmentioning

confidence: 99%

Systematic Comparison of Path Planning Algorithms using PathBench

Hsueh¹,

Toma²,

Jaafar³

et al. 2022

Preprint

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Path planning is an essential component of mobile robotics. Classical path planning algorithms, such as wavefront and rapidly-exploring random tree (RRT) are used heavily in autonomous robots. With the recent advances in machine learning, development of learning-based path planning algorithms has been experiencing a rapid growth. An unified path planning interface that facilitates the development and benchmarking of existing and new algorithms is needed. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learning-based path planning algorithms in 2D and 3D grid world environments. Many existing path planning algorithms are supported; e.g. A*, Dijkstra, waypoint planning networks, value iteration networks, gated path planning networks; and integrating new algorithms is easy and clearly specified. The benchmarking ability of PathBench is explored in this paper by comparing algorithms across five different hardware systems and three different map types, including built-in PathBench maps, video game maps, and maps from real world databases. Metrics, such as path length, success rate, and computational time, were used to evaluate algorithms. Algorithmic analysis was also performed on a real world robot to demonstrate PathBench's support for Robot Operating System (ROS). PathBench is open source 1 .

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“…6) Bagging LSTM [42]. This algorithm uses an ensemble approach [43] to get the best out of Online and CAE-LSTM.…”

Section: B Learned Planning Algorithmsmentioning

confidence: 99%

“…7) Waypoint Planning Networks (WPN) [42]. WPN integrates three ML-based planning algorithms; Online LSTM, CAE-LSTM, and bagging LSTM; with a waypoint module.…”

Section: B Learned Planning Algorithmsmentioning

confidence: 99%

PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms

Toma

Hsueh

Jaafar

et al. 2021

Preprint

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Path planning is a key component in mobile robotics. A wide range of path planning algorithms exist, but few attempts have been made to benchmark the algorithms holistically or unify their interface. Moreover, with the recent advances in deep neural networks, there is an urgent need to facilitate the development and benchmarking of such learning-based planning algorithms. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learned 2D and 3D path planning algorithms, while offering support for Robot Operating System (ROS). Many existing path planning algorithms are supported; e.g. A*, wavefront, rapidly-exploring random tree, value iteration networks, gated path planning networks; and integrating new algorithms is easy and clearly specified. We demonstrate the benchmarking capability of PathBench by comparing implemented classical and learned algorithms for metrics, such as path length, success rate, computational time and path deviation. These evaluations are done on built-in PathBench maps and external path planning environments from video games and real world databases. PathBench is open source 1 .

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Waypoint Planning Networks

Cited by 11 publications

References 36 publications

Autonomous aerial robotics for package delivery: A technical review

Autonomous aerial robotics for package delivery: A technical review

Systematic Comparison of Path Planning Algorithms using PathBench

PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms

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