arXiv subjects

Behzad Zamani

Publications and source records attributed to Behzad Zamani.

5 recordsLinked to original sources

arXiv2025

GMM-Based Time-Varying Coverage Control

In coverage control problems that involve time-varying density functions, the coverage control law depends on spatial integrals of the time evolution of the density function. The latter is often neglected, replaced with an upper bound or calculated as a numerical approximation of the spatial integrals involved. In this paper, we consider a special case of time-varying density functions modeled as Gaussian Mixture Models (GMMs) that evolve with time via a set of time-varying sources (with known corresponding velocities). By imposing this structure, we obtain an efficient time-varying coverage controller that fully incorporates the time evolution of the density function. We show that the induced trajectories under our control law minimise the overall coverage cost. We elicit the structure of the proposed controller and compare it with a classical time-varying coverage controller, against which we benchmark the coverage performance in simulation. Furthermore, we highlight that the computationally efficient and distributed nature of the proposed control law makes it ideal for multi-vehicle robotic applications involving time-varying coverage control problems. We employ our method in plume monitoring using a swarm of drones. In an experimental field trial we show that drones guided by the proposed controller are able to track a simulated time-varying chemical plume in a distributed manner.

eess.SY↗

arXiv2025

Modular Robot and Landmark Localisation Using Relative Bearing Measurements

In this paper we propose a modular nonlinear least squares filtering approach for systems composed of independent subsystems. The state and error covariance estimate of each subsystem is updated independently, even when a relative measurement simultaneously depends on the states of multiple subsystems. We integrate the Covariance Intersection (CI) algorithm as part of our solution in order to prevent double counting of information when subsystems share estimates with each other. An alternative derivation of the CI algorithm based on least squares estimation makes this integration possible. We particularise the proposed approach to the robot-landmark localization problem. In this problem, noisy measurements of the bearing angle to a stationary landmark position measured relative to the SE(2) pose of a moving robot couple the estimation problems for the robot pose and the landmark position. In a randomized simulation study, we benchmark the proposed modular method against a monolithic joint state filter to elucidate their respective trade-offs. In this study we also include variants of the proposed method that achieve a graceful degradation of performance with reduced communication and bandwidth requirements.

cs.RO↗

arXiv2025

Conservative fusion of unbiased partial state estimates: CI is optimal

We show that Covariance Intersection (CI) is optimal amongst all conservative unbiased linear fusion rules also in the general case of information fusion of two unbiased partial state estimates, significantly generalizing the known optimality result for fusion of full state estimates. In fact, we prove the much stronger result that three different optimization problems are equivalent, namely the abstract optimal conservative unbiased linear information fusion problem with respect to a strictly isotone cost function, the scalar CI problem, and a simple semi-definite program. We provide a general solvability condition for these problems as well as equations characterizing the optimal solutions for the matrix determinant and matrix trace cost functions.

math.OC↗

arXiv2025

Modular Decomposition of Hierarchical Finite State Machines

Hierarchical Finite State Machines (HFSMs) are a standard software-modelling concept which extends the classical Finite State Machine (FSM) notion with the useful abstraction of hierarchical nesting. That is, an HFSM is an FSM whose states can be other FSMs. The hierarchy in HFSMs is provided at design time, and can be removed by expanding nested states, allowing HFSMs to inherit the semantics of FSMs. However, because hierarchy is a useful representation of the structure of an FSM, we would like to be able to invert this operation: given an FSM, can we compute equivalent HFSMs? This is the topic of this paper. By adapting the analogous theory of `modular decomposition' from graph theory into automata theory, we are able to compute an efficient representation of the space of equivalent HFSMs to a given one. Specifically, we first define a module of an FSM, which is a collection of nodes which can be treated as a nested FSM. Unlike modules in graphs, some modules in FSMs are lacking in algebraic structure. We identify a simple and natural restriction of the modules, called thin modules, which regain many of the critical properties from modules in graphs. We then construct a linear-space directed graph which uniquely represents every thin module, and hence every equivalent (thin) HFSM. We call this graph the modular decomposition. The modular decomposition makes clear the significant common structure underlying equivalent thin HFSMs. We provide an $O(n^2k)$ algorithm for constructing the modular decomposition of an $n$-state $k$-symbol FSM. We demonstrate the applicability of this theory on the following `bottleneck' problem: given an HFSM, find an equivalent one where the size of the largest component FSM is minimised. The modular decomposition gives a simple greedy algorithm for the bottleneck problem on thin HFSMs, which we demonstrate on a wristwatch HFSM example from Harel (1987).

cs.FL↗

arXiv2024

Geometric Data Fusion for Collaborative Attitude Estimation

In this paper, we consider the collaborative attitude estimation problem for a multi-agent system. The agents are equipped with sensors that provide directional measurements and relative attitude measurements. We present a bottom-up approach where each agent runs an extended Kalman filter (EKF) locally using directional measurements and augments this with relative attitude measurements provided by neighbouring agents. The covariance estimates of the relative attitude measurements are geometrically corrected to compensate for relative attitude between the agent that makes the measurement and the agent that uses the measurement before being fused with the local estimate using the convex combination ellipsoid (CCE) method to avoid data incest. Simulations are undertaken to numerically evaluate the performance of the proposed algorithm.

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