Information Theoretic Source Seeking Strategies for Multiagent Plume Tracking in Turbulent Fields

Hajieghrary, Hadi; Mox, Daniel; Hsieh, M. Ani

doi:10.3390/jmse5010003

Cited by 14 publications

(5 citation statements)

References 25 publications

(42 reference statements)

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“…A feasible way should be taken to measure the cognition difference by avoiding the exchange of entire probability maps in the future. The particle filter method can be introduced to approximate the posterior probability map [17,27]. Consequently, there is no need to exchange the whole probability maps but a limited number of the sampled particles.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, the source probability map that depends solely on its own sporadic observations is updated slowly without incorporating the conspecifics' observations. Thus, individuals tend to synthesize the sporadic and intermittent observations of the group to optimize their exploration strategy [23,24,27]. However, in the presence of sensory noises or false signals, the probability map that integrates the observations of the group can easily be polluted by inadequate observations from certain conspecific and drifts away from the source.…”

Section: Introductionmentioning

confidence: 99%

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Multi-agent collaborative infotaxis search based on cognition difference

Cheng

Ristić

et al. 2019

J. Phys. A: Math. Theor.

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Searching for a signal-emitting source in a turbulent medium is challenging due to sporadic and intermittent measurements. Individual searching agents through sharing information and collaboration can achieve a very effective, flexible and noise-tolerant search in turbulent environments. In this work, by extending the single-agent infotaxis to multi-agent systems, a collaborative infotaxis strategy is proposed to synthesize the spatio-temporal sensing capabilities of a group of agents and optimize the search time. Each agent adjusts its coupling strength with conspecifics and incorporates the conspecific’s observations with dynamic heterogeneous weights according to cognition differences. The cognition difference between agents is measured by the dissimilarity of probability maps. When each agent determines its actions by maximizing the synthesized information of the group, efficient and flexible search behaviors of the group are manifested in various scenarios. The proposed strategy can provide an efficient approach to the investigation of the collaborative behaviors of biological species.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-agent collaborative infotaxis search based on cognition difference

Cheng

Ristić

et al. 2019

J. Phys. A: Math. Theor.

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“…To facilitate the computation intensity, the sequential Monte Carlo method is employed to represent the probability distribution with a limited and tractable amount of randomly-drawn particles. The use of a particle filter allows us to bound the computational burden on the sensor platform [16,23], which determines the probability distribution to cover the search area that is of interest.…”

Section: Free Energy Infotaxis Search Schemementioning

confidence: 99%

“…The varying temperature is reduced by levering the trace of the covariance matrix of the probability distribution and so shifts the balance toward exploitation with the receding of the uncertainty or the increasing reliable estimation. Similar to [16,17,23], we employ a particle filter representation of the source probability distribution to make the strategy computationally tractable for large complex spaces. Then, the potential energy is computed by the spread of the particles and the distance between the current position and all the particles.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Searching for a Diffusive Source Based on Minimizing the Combination of Entropy and Potential Energy

Cheng

Lei

2019

Sensors

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The infotaxis scheme is a search strategy for a diffusive source, where the sensor platform is driven to reduce the uncertainty about the source through climbing the information gradient. The infotaxis scheme has been successfully applied in many source searching tasks and has demonstrated fast and stable searching capabilities. However, the infotaxis scheme focuses on gathering information to reduce the uncertainty down to zero, rather than chasing the most probable estimated source when a reliable estimation is obtained. This leads the sensor to spend more time exploring the space and yields a longer search path. In this paper, from the context of exploration-exploitation balance, a novel search scheme based on minimizing free energy that combines the entropy and the potential energy is proposed. The term entropy is implemented as the exploration to gather more information. The term potential energy, leveraging the distance to the estimated sources, is implemented as the exploitation to reinforce the chasing behavior with the receding of the uncertainty. It results in a faster effective search strategy by which the sensor determines its actions by minimizing the free energy rather than only the entropy in traditional infotaxis. Simulations of the source search task based on the computational plume verify the efficiency of the proposed strategy, achieving a shorter mean search time.

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“…The authors wish to correct the Acknowledgments section in their paper [1] We apologize for any inconvenience this may cause. The change does not affect the scientific results.…”

mentioning

confidence: 99%