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Dirafzoon et al  explored the effect of WF behavior on the exploration and mapping process. ... Dirafzoon  , (c) natural motion model for the biobots in terms of correlated random walk (CRW), (d) encounter region for two biobots. robotic systems may not be able to reach certain locations under ...arXiv:1607.00051v1 fatcat:ecr623dbtbdx7cbl72rdbyhdni
In this paper, we present an approach for dynamic exploration and mapping of unknown environments using a swarm of biobotic sensing agents, with a stochastic natural motion model and a leading agent (e.g., an unmanned aerial vehicle). The proposed robust mapping technique constructs a topological map of the environment using only encounter information from the swarm. A sliding window strategy is adopted in conjunction with a topological mapping strategy based on local interactions among thearXiv:1507.03206v2 fatcat:6hhb2nwgvjd55hcy53mnvmiob4
more »... m in a coordinate-free fashion to obtain local maps of the environment. These maps are then merged into a global topological map which can be visualized using a graphical representation that integrates geometric as well as topological feature of the environment. Localized robust topological features are extracted using tools from topological data analysis. Simulation results have been presented to illustrate and verify the correctness of our dynamic mapping algorithm.
In this paper, we exploit minimal sensing information gathered from biologically inspired sensor networks to perform exploration and mapping in an unknown environment. A probabilistic motion model of mobile sensing nodes, inspired by motion characteristics of cockroaches, is utilized to extract weak encounter information in order to build a topological representation of the environment. Neighbor to neighbor interactions among the nodes are exploited to build point clouds representing spatialarXiv:1410.4622v1 fatcat:nss2bwrbijfpxhemaz52fxwnn4
more »... tures of the manifold characterizing the environment based on the sampled data. To extract dominant features from sampled data, topological data analysis is used to produce persistence intervals for features, to be used for topological mapping. In order to improve robustness characteristics of the sampled data with respect to outliers, density based subsampling algorithms are employed. Moreover, a robust scale-invariant classification algorithm for persistence diagrams is proposed to provide a quantitative representation of desired features in the data. Furthermore, various strategies for defining encounter metrics with different degrees of information regarding agents' motion are suggested to enhance the precision of the estimation and classification performance of the topological method.
Alireza Dirafzoon received his B.Sc. degree in Electrical Engineering in 2007 from Amirkabir University of Technology, Tehran, Iran. ...doi:10.1587/transinf.e94.d.3 fatcat:ej4goakwgfdcdlg43hwsj32pga
Dirafzoon and E. Lobaton are with the department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27606, USA. ...doi:10.1109/iros.2013.6697160 dblp:conf/iros/DirafzoonL13 fatcat:gmznacy2ijcpdc7v4hymbqpzla