Abstract
This paper proposed a hybrid vision-based robot control strategy for micro ground robots by mediating two vision models from mixed categories: a bio-inspired collision avoidance model and a segmentation based target following model. The implemented model coordination strategy is described as a probabilistic model using finite state machine (FSM) that allows the robot to switch behaviours adapting to the acquired visual information. Experiments demonstrated the stability and convergence of the embedded hybrid system by real robots, including the studying of collective behaviour by a swarm of such robots with environment mediation. This research enables micro robots to run visual models with more complexity. Moreover, it showed the possibility to realize aggregation behaviour on micro robots by utilizing vision as the only sensing modality from non-omnidirectional cameras.
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Acknowledgement
This work is supported by EU-FP7-IRSES project HAZCEPT(318907) and Horizon2020 project STEP2DYNA.
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Hu, C., Fu, Q., Liu, T., Yue, S. (2018). A Hybrid Visual-Model Based Robot Control Strategy for Micro Ground Robots. In: Manoonpong, P., Larsen, J., Xiong, X., Hallam, J., Triesch, J. (eds) From Animals to Animats 15. SAB 2018. Lecture Notes in Computer Science(), vol 10994. Springer, Cham. https://doi.org/10.1007/978-3-319-97628-0_14
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