Online Aerial 2.5D Terrain Mapping and Active Aerial Vehicle Exploration for Ground Robot Navigation

Wagner, Anthony; Peterson, John; Donnelly, James F.; Chourey, Shivam; Kochersberger, Kevin

doi:10.1007/s10846-022-01751-9

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Mapless planners [3][4][5]7] only employ a local map that is just enough for local planning, and a memoryless one [6] plans directly on sensor data. Distinguishing from those, map-based planning systems [8,9] generally integrate global maps [10,11] a priori and a global planning algorithm such as RRT* [12] to guarantee planning completeness in tasks such as exploration, [13][14][15][16][17] or navigating toward a goal [18][19][20]. All mapless and memoryless planners cannot access any map but use sensor data.…”

Section: Introductionmentioning

confidence: 99%

Depth-based Sampling and Steering Constraints for Memoryless Local Planners

Nguyen¹,

Nguyen²,

Choudhury³

et al. 2022

Preprint

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By utilizing only depth information, the paper introduces a novel but efficient local planning approach that enhances not only computational efficiency but also planning performances for memoryless local planners. The sampling is first proposed to be based on the depth data which can identify and eliminate a specific type of in-collision trajectories in the sampled motion primitive library. More specifically, all the obscured primitives' endpoints are found through querying the depth values and excluded from the sampled set, which can significantly reduce the computational workload required in collision checking. On the other hand, we furthermore propose a steering mechanism also based on the depth information to effectively prevent an autonomous vehicle from getting stuck when facing a large convex obstacle, providing a higher level of autonomy for a planning system. Our steering technique is theoretically proved to be complete in scenarios of convex obstacles. To evaluate effectiveness of the proposed DEpth based both Sampling and Steering (DESS) methods, we implemented them in the synthetic environments where a quadrotor

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Section: Introductionmentioning

confidence: 99%

Depth-based Sampling and Steering Constraints for Memoryless Local Planners

Nguyen¹,

Nguyen²,

Choudhury³

et al. 2022

Preprint

View full text Add to dashboard Cite

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Haptic Feedback Remote Control System for Electric Mechanical Assembly Vehicle Developed to Avoid Obstacles

Kowol

Nowak

Banas

et al. 2023

J Intell Robot Syst

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Depth-based Sampling and Steering Constraints for Memoryless Local Planners

Nguyen,

Choudhury

et al. 2023

J Intell Robot Syst

View full text Add to dashboard Cite

By utilizing only depth information, the paper introduces a novel two-stage planning approach that enhances computational efficiency and planning performances for memoryless local planners. First, a depth-based sampling technique is proposed to identify and eliminate a specific type of in-collision trajectories among sampled candidates. Specifically, all trajectories that have obscured endpoints are found through querying the depth values and will then be excluded from the sampled set, which can significantly reduce the computational workload required in collision checking. Subsequently, we apply a tailored local planning algorithm that employs a direction cost function and a depth-based steering mechanism to prevent the robot from being trapped in local minima. Our planning algorithm is theoretically proven to be complete in convex obstacle scenarios. To validate the effectiveness of our DEpth-based both Sampling and Steering (DESS) approaches, we conducted experiments in simulated environments where a quadrotor flew through cluttered regions with multiple various-sized obstacles. The experimental results show that DESS significantly reduces computation time in local planning compared to the uniform sampling method, resulting in the planned trajectory with a lower minimized cost. More importantly, our success rates for navigation to different destinations in testing scenarios are improved considerably compared to the fixed-yawing approach.

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Online Aerial 2.5D Terrain Mapping and Active Aerial Vehicle Exploration for Ground Robot Navigation

Cited by 5 publications

References 24 publications

Depth-based Sampling and Steering Constraints for Memoryless Local Planners

Depth-based Sampling and Steering Constraints for Memoryless Local Planners

Haptic Feedback Remote Control System for Electric Mechanical Assembly Vehicle Developed to Avoid Obstacles

Depth-based Sampling and Steering Constraints for Memoryless Local Planners

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