What question did this study set out to answer?

The aim is to develop a navigation framework for automated guided vehicles (AGVs) that ensures autonomous and collision-free motion in structured environments.

July 10, 2026Robotica

An autonomous navigation framework and strategy for collision-free motion planning of automated guided vehicles in structured environments

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Authors

MAMuhammad AizatUniversiti Sains Malaysia CKChia Siang KokWestern Digital (Japan)MZMohd ZulkifliTelekom Malaysia Berhad (Malaysia)

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Implication

Randomized trial evaluates collision-free motion planning in automated guided vehicles, indicating improved navigation safety and efficiency.

Key Points

The aim is to develop a navigation framework for automated guided vehicles (AGVs) that ensures autonomous and collision-free motion in structured environments.
Developed a global path planner using the Probabilistic Roadmap algorithm
Implemented a local trajectory tracker with the Pure Pursuit controller
Utilized the Vector Field Histogram algorithm for real-time obstacle avoidance
Achieved a final positioning accuracy of 99.40% with an average final percentage error of 0.61% in free-path navigation
Maintained high final positioning accuracy of 99.10% in obstacle-path navigation under varying configurations
Demonstrated successful coordination of PRM, PP, and VFH for adaptive, safe navigation

Cite This Study

Aizat et al. (2026) studied this question.

synapsesocial.com/papers/6a508ea56eeac72a437a15ac https://doi.org/10.1017/s0263574726103609

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July 10, 2026Robotica

An autonomous navigation framework and strategy for collision-free motion planning of automated guided vehicles in structured environments

View Full Paper

Ask AI

Bookmark

Authors

MAMuhammad AizatUniversiti Sains Malaysia CKChia Siang KokWestern Digital (Japan)MZMohd ZulkifliTelekom Malaysia Berhad (Malaysia)

Discussion

Member takes

Implication

Randomized trial evaluates collision-free motion planning in automated guided vehicles, indicating improved navigation safety and efficiency.

Key Points

The aim is to develop a navigation framework for automated guided vehicles (AGVs) that ensures autonomous and collision-free motion in structured environments.
Developed a global path planner using the Probabilistic Roadmap algorithm
Implemented a local trajectory tracker with the Pure Pursuit controller
Utilized the Vector Field Histogram algorithm for real-time obstacle avoidance
Achieved a final positioning accuracy of 99.40% with an average final percentage error of 0.61% in free-path navigation
Maintained high final positioning accuracy of 99.10% in obstacle-path navigation under varying configurations
Demonstrated successful coordination of PRM, PP, and VFH for adaptive, safe navigation

Cite This Study

Aizat et al. (2026) studied this question.

synapsesocial.com/papers/6a508ea56eeac72a437a15ac https://doi.org/10.1017/s0263574726103609

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