Benchmark of Sampling-Based Optimizing Planners for Outdoor Robot Navigation

Atas, Fetullah; Cielniak, Grzegorz; Grimstad, Lars

doi:10.1007/978-3-031-22216-0_16

Cited by 1 publication

(1 citation statement)

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“…Random sampling-based path planning algorithms generally consist of BITs and RRTs. Regionally Accelerated Batch Informed Trees (RABIT) are more widely used and perform better in high dimensional and dynamic environments [134] in comparison to graph search-based path planning algorithms. Bionic-based intelligent robot path planning methods simulate the behaviours of insects to generate evolving paths.…”

Section: Mobile Robot Local Path Planningmentioning

confidence: 99%

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Wijayathunga,

Rassau,

Chai

2023

Applied Sciences

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The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

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Section: Mobile Robot Local Path Planningmentioning

confidence: 99%