An Extension of BIM Using AI: a Multi Working-Machines Pathfinding Solution

Xiang, Yusheng; Liu, Kailun; Su, Tianqing; Jun, Li; Ouyang, Shirui; Mao, Samuel S.; Geimer, Marcus

doi:10.48550/arxiv.2105.06635

Cited by 1 publication

(3 citation statements)

References 44 publications

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“…We focus our research on the path-planning algorithm for autonomous earth-moving vehicles. The most successful algorithms for these problems use classic solutions similar to [6], [30], [13], [33]. In [6], the authors implemented a heuristic approach to reach the piles and examined the trade-off between increasing the blade's grading of the pile to full capacity and pushing less sand to reduce the elapsed time and sand spillage.…”

Section: Related Workmentioning

confidence: 99%

“…By combining these graphs and using basic rule-based logic, they found the optimal path for moving multiple objects to multiple goals. Finally, [33] attempted to solve the multi-agent problem by using graphs to describe the environment and agents in the scene and simple optimizations to determine the location of each agent.…”

Section: Related Workmentioning

confidence: 99%

“…Problems of this sort in the off-road autonomous driving industry have attracted increasing interest in the past two decades due to shortage in experienced drivers as well as risings demands in the construction industry. Previous work has mainly focused on obstacle avoidance [12], [33], optimal trajectory planning [35], [30], [33] and traversability [36].…”

Section: Introductionmentioning

confidence: 99%

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AGPNet -- Autonomous Grading Policy Network

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Miron²,

Goldfracht³

et al. 2021

Preprint

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Fig. 1. Comparison of the ability of a simulated environment and a real dozer to grade an area studded with piles. Top row: RGB images of our experimental setup (see Section III-C) showing the scaled dozer facing the sand piles. Middle row: Representative height-maps of states in the real environment. Depth observations were projected onto world coordinates using orthographic projections [4]. Bottom row: Similar states observed in the simulated environment. In the middle and bottom rows, the right column is the initial state space, and all the others, states after actions were taken in the simulated and real environments. This figure clearly depicts the resemblance between the simulated and real height-maps in the grading task.

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