GPS Backpack for Multi-drone Reconstruction Project
This project is a subsidiary project under the multi-drone project, its purpose is to help drones locate the location of the test person, has completed the tracking shot.
This project is a subsidiary project under the multi-drone project, its purpose is to help drones locate the location of the test person, has completed the tracking shot.
a re-implementation of the GUided Sampling Tree (GUST) algorithm, originally developed by [Plaku(2015)]
In this study, our focus is on maximizing the robots’ coverage of the targets.
Robot Juggling (Learn from demonstration)
Published in AAAI, 2024
The Multi-Agent Path Finding (MAPF) problem involves planning collision-free paths for multiple agents in a shared environment. The majority of MAPF solvers rely on the assumption that an agent can arrive at a specific location at a specific timestep. However, real-world execution uncertainties can cause agents to deviate from this assumption, leading to collisions and deadlocks. Prior research solves this problem by having agents follow a Temporal Plan Graph (TPG), enforcing a consistent passing order at every location as defined in the MAPF plan. However, we show that TPGs are overly strict because, in some circumstances, satisfying the passing order requires agents to wait unnecessarily, leading to longer execution time. To overcome this issue, we introduce a new graphical representation called a Bidirectional Temporal Plan Graph (BTPG), which allows switching passing orders during execution to avoid unnecessary waiting time. We design two anytime algorithms for constructing a BTPG: BTPG-naïve and BTPG-optimized. Experimental results show that following BTPGs consistently outperforms following TPGs, reducing unnecessary waits by 8-20%.
Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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