July 25, 2024Engineering Research ExpressOpen Access

Optimal path planning using bidirectional rapidly-exploring random tree star-dynamic window approach (BRRT*-DWA) with adaptive Monte Carlo localization (AMCL)

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Enhanced path planning performance is achieved in dynamic environments compared with state-of-the-art algorithms, ensuring optimal obstacle avoidance during navigation.
Simulation and experimental testing integrates adaptive monte carlo localization with the dynamic window approach, using light detection and ranging sensor data for mapping.
Highlights potential to improve mobile service robot autonomy in unknown dynamic settings, while simultaneous localization and mapping enables robust spatial tracking.

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Implication

Simulation and experimental study demonstrates improved path planning in unknown dynamic environments, suggesting enhanced navigation for mobile service robots.

Cite This Study

Asfaw et al. (2024) studied this question.

synapsesocial.com/papers/68e5f0a2b6db6435875850ae https://doi.org/10.1088/2631-8695/ad61bd

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Implication

Simulation and experimental study demonstrates improved path planning in unknown dynamic environments, suggesting enhanced navigation for mobile service robots.

Optimal path planning using bidirectional rapidly-exploring random tree star-dynamic window approach (BRRT*-DWA) with adaptive Monte Carlo localization (AMCL)

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Discussion

Implication

Cite This Study

Optimal path planning using bidirectional rapidly-exploring random tree star-dynamic window approach (BRRT*-DWA) with adaptive Monte Carlo localization (AMCL)

Key Points

Discussion

Implication

Cite This Study