Multi-agent exploration of spatial dynamical processes under sparsity constraints

Wiedemann, Thomas; Manss, Christoph; Shutin, Dmitriy

doi:10.1007/s10458-017-9375-7

Cited by 19 publications

(33 citation statements)

References 36 publications

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“…Since finding gas sources is also a challenge for animals like moths, beetles 40 or bacteria, exploration strategies are often bio inspired, e.g. [6], [7].…”

Section: Background and Related Workmentioning

confidence: 99%

“…This strategy guides the robots to their measurement locations. In contrast to the greedy algorithm in [23] where 105 the robots only consider their direct neighborhood for a new measurement, in this paper robots possess a global view of the whole environment.…”

mentioning

confidence: 99%

“…The methods and theory for modeling the PDE in a probabilistic framework and the algorithms for a distributed implementation were firstly introduced in [23]. This article is a substantial extension of a previous conference paper 110 [24].…”

mentioning

confidence: 99%

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Model-based gas source localization strategy for a cooperative multi-robot system—A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties

Wiedemann

Shutin

Lilienthal

2019

Robotics and Autonomous Systems

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PreprintThis is the submitted version of a paper published in Robotics and Autonomous Systems.Citation for the original published paper (version of record):Wiedemann, T., Shutin, D., Lilienthal, A J. (2019) Model-based gas source localization strategy for a cooperative multi-robot system-A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties Robotics and Autonomous Systems, Abstract Sampling gas distributions by robotic platforms in order to find gas sources is an appealing approach to alleviate threats for a human operator. Different sampling strategies for robotic gas exploration exist. In this paper we investigate the benefit that could be obtained by incorporating physical knowledge about the gas dispersion. By exploring a gas diffusion process using a multirobot system. The physical behavior of the diffusion process is modeled using a Partial Differential Equation (PDE) which is integrated into the exploration strategy. It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The objective of the exploration strategy is to guide the robots to informative measurements location and by means of concentration measurements estimate the source parameters, in particular, their number, locations and magnitudes. To this end we propose a probabilistic approach towards PDE identification under sparsity constraints using factor graphs and a message passing algorithm. Moreover, message passing schemes permit efficient distributed implementation of the algorithm, which makes it suitable for a multi-robot system. We designed a experimental setup that allows us to evaluate the performance of the exploration strategy in hardware-in-the-loop experiments as well as in experiments with real ethanol gas under laboratory conditions. The results indicate that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling.

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“…Since finding gas sources is also a challenge for animals like moths, beetles 40 or bacteria, exploration strategies are often bio inspired, e.g. [6], [7].…”

Section: Background and Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Model-based gas source localization strategy for a cooperative multi-robot system—A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties

Wiedemann

Shutin

Lilienthal

2019

Robotics and Autonomous Systems

Self Cite

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“…It is known that using non-negative elements is connected to so-called compressed sensing ( [15]). Compressed sensing (CS) ( [16,17,18,19,20]) is a method used in technical applications to reconstruct underlying causes from data if these causes are sparse. CS is used to find solutions to otherwise under-determined linear systems using only a minimum of measurements.…”

Section: Introductionmentioning

confidence: 99%

Back-propagation learning in deep Spike-By-Spike networks

Rotermund

Pawelzik

2019

Preprint

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Neural networks are important building blocks in artificial intelligence applications. These technical systems rely on noiseless continuous signals in stark contrast to the discrete action potentials exchanged among the neurons in real brains. A promising approach towards bridging this gap are the Spike-by-Spike (SbS) networks, which were demonstrated to reliably process information based on few stochastic spikes given suitable synaptic weights are provided. What is missing are algorithms for finding weight sets that would optimize the output performance of acyclically connected SbS networks.Here, a learning rule for hierarchically organized SbS networks is derived. It is inspired by the error backpropagation algorithms used in feed-forward neural networks. The properties of this approach are investigated and its functionality demonstrated by simulations. In particular, a Deep Convolutional SbS network for classifying handwritten digits (MNIST) is presented. The resulting classification performance reaches values achieved by standard deep networks that have a similar structure and also use only simple gradient descent. We expect that this new SbS back-prop learning rule will provide a basis for building spiking neuronal network models for research in neuroscience and technical applications, especially when it will become combined with state-of-the-art optimization techniques and implemented on specialized computational hardware.

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“…Another recent discovery in the field of machine learning is compressed sensing ( [10]), which allows to reconstruct underlying causes from incomplete measurements if the underlying causes are sparse. This lead to applications in many fields from reducing measurement time for Magnetic Resonance Imaging ( [11]) to building swarms of robots for efficiently exploring other planets ( [12]). Sparseness and its benefits for information processing is also an important topic in brain research ( [13,14,15]).…”

Section: Introductionmentioning

confidence: 99%

Massively Parallel FPGA Hardware for Spike-By-Spike Networks

Rotermund

2018

Preprint

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2While inspired by the brain, currently successful artificial neural networks lack key features of 3 the biological original. In particular, the deep convolutional networks (DCNs) neither use pulses 4 as signals exchanged among neurons, nor do they include recurrent connections which are 5 both core properties of real neuronal networks. This not only puts to question the relevance of 6 DCNs for explaining information processing in nervous systems but also limits their potential for 7 modeling natural intelligence. 8 Spike-By-Spike (SbS) networks are a promising new approach that combines the 9 computational power of artificial networks with biological realism. Instead of separate neurons 10 they consist of neuronal populations performing inference. Even though the underlying equations 11 are rather simple implementations of such networks on currently available hardware are several 12 orders of magnitude slower than for comparable non-spiking deep networks. 13 Here, we develop and investigate a framework for SbS networks on chip. Thanks to the 14 communication via spikes, already moderately sized deep networks based on the SbS approach 15 allows a parallelization into thousands of simple and fully independent computational cores. We 16 demonstrate the feasibility of our design on a Xilinx Virtex 6 FPGA while avoiding proprietary 17 cores (except block memory) that can not be realized on a custom-designed ASIC. We present 18 memory access optimized circuits for updating the internal variables of the neurons based on 19 incoming spikes as well as for learning the connection's strength. The optimized computational 20 circuits as well as the representation of variables fully exploit the non-negative properties of all 21 data in the SbS approach. We compare the sizes of the arising circuits for floating and fixed point 22 numbers. In addition we show how to minimize the number of components that are required for 23 the computational cores by reusing their components for different functions. 24 INTRODUCTIONNowadays, deep neuronal networks (Schmidhuber, 2015) are a basis for successfully applying neuronal 25 networks on problems from artificial intelligence research (Azkarate Saiz, 2015; Silver et al., 2016; 26 Guo et al., 2016; Gatys et al., 2016). The revival of using neuronal networks was provoked by the increase 27 of computational powers in modern computers and boosted even more through modern 3D graphic cards 28 as well as specialized application specific integrated circuits (ASICs) (Sze et al., 2017; Jouppi et al., 2018) 29 and field programmable gate arrays (FPGAs) (Lacey et al., 2016). The most successful type of networks 30 is based on multilayer perceptrons (Rumelhart et al., 1986; Rosenblatt, 1958) and consists of several so 31 Rotermund & Pawelzik SbS FPGAcalled hidden layers. Typically information is processed and feed forward from one hidden layer to the 32 next, beginning at the input layer and ending at the network's output layer. In theory such a network is 33 able to c...

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Multi-agent exploration of spatial dynamical processes under sparsity constraints

Cited by 19 publications

References 36 publications

Model-based gas source localization strategy for a cooperative multi-robot system—A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties

Model-based gas source localization strategy for a cooperative multi-robot system—A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties

Back-propagation learning in deep Spike-By-Spike networks

Massively Parallel FPGA Hardware for Spike-By-Spike Networks

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