From cc57a028f3c25d39c8d2dabe77629d3d637cf24a Mon Sep 17 00:00:00 2001 From: Devansh Ramgopal Agrawal Date: Mon, 9 Sep 2024 21:48:40 -0400 Subject: [PATCH] updated multiagent coverage controls paper --- content/papers/2024-multiagent-coverage.md | 16 ++-------------- static/pdfs/2024-multiagent-coverage.pdf | Bin 1394578 -> 2764244 bytes 2 files changed, 2 insertions(+), 14 deletions(-) diff --git a/content/papers/2024-multiagent-coverage.md b/content/papers/2024-multiagent-coverage.md index 43624ae..2fa52d1 100644 --- a/content/papers/2024-multiagent-coverage.md +++ b/content/papers/2024-multiagent-coverage.md @@ -9,18 +9,6 @@ authors: - dimitrapanagou arxiv: https://arxiv.org/abs/2403.17917v1 code: https://github.com/dev10110/multiagent-clarity-based-dynamic-coverage/ -abstract: "This paper presents two algorithms for multi-agent dynamic coverage in spatiotemporal environments, where -the coverage algorithms are informed by the method of data -assimilation. In particular, we show that by considering the -information assimilation algorithm, here a Numerical Gaussian -Process Kalman Filter, the influence of measurements taken -at one position on the uncertainty of the estimate at another -location can be computed. We use this relationship to propose -new coverage algorithms. Furthermore, we show that the con- -trollers naturally extend to the multi-agent context, allowing for -a distributed-control central-information paradigm for multi- -agent coverage. Finally, we demonstrate the algorithms through -a realistic simulation of a team of UAVs collecting wind data -over a region in Austria." +abstract: "This paper presents two algorithms for multi-agent dynamic coverage in spatiotemporal environments, where the coverage algorithms are informed by the method of data assimilation. In particular, we show that by explicitly modeling the environment using a Gaussian Process (GP) model, and considering the sensing capabilities and the dynamics of a team of robots, we can design an estimation algorithm and multi-agent coverage controller that explores and estimates the state of the spatiotemporal environment. The uncertainty of the estimate is quantified using clarity, an information-theoretic metric, where higher clarity corresponds to lower uncertainty. By exploiting the relationship between GPs and Stochastic Differential Equations (SDEs) we quantify the increase in clarity of the estimated state at any position due to a measurement taken from any other position. We use this relationship to design two new coverage controllers, both of which scale well with the number of agents exploring the domain, assuming the robots can share the map of the clarity over the spatial domain via communication. We demonstrate the algorithms through a realistic simulation of a team of robots collecting wind data over a region in Austria." pdf: /pdfs/2024-multiagent-coverage.pdf ---- \ No newline at end of file +--- diff --git a/static/pdfs/2024-multiagent-coverage.pdf b/static/pdfs/2024-multiagent-coverage.pdf index 0751f2cc97e6a2124a3fbc5fe66a8ffc294cce9a..efb7ceaa3b4415d864003d2fc04ad4b1c8f4ae7e 100644 GIT binary patch literal 2764244 zcma&sLz6HJupQvBZQHhOoA21RZQHhO+qP}np2?R~cDal6s((Ru)pHK1f`}L`BONOg z>HPA@Ius`}0Rw@Zp(PX#4-~zOiLIHlIRPsJJ14>a87O)&3u|W+M*?~=YXfH!5fdXj zV-qMoJ}4(=M-u}ZDEG}3EorA?F~{C3_50J(py5Nskg?l=(&2?`>)}Z#*cPw+V+aJ; zM#Lbne*phbUaoR7^w98j%x7w^?bp+4E31VH;_s#>e;*H`_`Px8PpO_7Iilc%|FZSQm^L-lgjXVhJGRfo22c3e*FbT9M;J1H4m{#K8_D(78j{g&sxm0drg z=sapdaXs&zjP}$$f)?5u<+YN1^j^PF1x)t6GGv+gwDm7kyt%kclBt;Prmp4}rO(Oj zK5-cya)=&oOQ!08J<0P3>rjJc&~m;k^; zgPn75j*0)lJbsptVqL#-eN6A|T{d|UV&DhBoq1UZQ6R?-XE`Y_k!0K9cDO5KCs4~A 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