Multi Agent Framework for Collective Intelligence Research
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929469870047232 |
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| author | Dochian, Alexandru |
| author_facet | Dochian, Alexandru |
| contents | This paper presents a scalable decentralized multi agent framework that facilitates the exchange of information between computing units through computer networks. The architectural boundaries imposed by the tool make it suitable for collective intelligence research experiments ranging from agents that exchange hello world messages to virtual drone agents exchanging positions and eventually agents exchanging information via radio with real Crazyflie drones in VU Amsterdam laboratory. The field modulation theory is implemented to construct synthetic local perception maps for agents, which are constructed based on neighbouring agents positions and neighbouring points of interest dictated by the environment. By constraining the experimental setup to a 2D environment with discrete actions, constant velocity and parameters tailored to VU Amsterdam laboratory, UAV Crazyflie drones running hill climbing controller followed collision-free trajectories and bridged sim-to-real gap. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12391 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi Agent Framework for Collective Intelligence Research Dochian, Alexandru Robotics This paper presents a scalable decentralized multi agent framework that facilitates the exchange of information between computing units through computer networks. The architectural boundaries imposed by the tool make it suitable for collective intelligence research experiments ranging from agents that exchange hello world messages to virtual drone agents exchanging positions and eventually agents exchanging information via radio with real Crazyflie drones in VU Amsterdam laboratory. The field modulation theory is implemented to construct synthetic local perception maps for agents, which are constructed based on neighbouring agents positions and neighbouring points of interest dictated by the environment. By constraining the experimental setup to a 2D environment with discrete actions, constant velocity and parameters tailored to VU Amsterdam laboratory, UAV Crazyflie drones running hill climbing controller followed collision-free trajectories and bridged sim-to-real gap. |
| title | Multi Agent Framework for Collective Intelligence Research |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.12391 |