Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2305.08542

Design and Implementation of Emergency Simulated Lighting System Based on Tello UAV

Yexin Pan·Yong Xu·Bo Ma·Chuanhuang Li
View original source ↗Read source document ↗Export metadata ↓

Abstract

In recent years, with the increasing maturity of UAV technology, the application of UAV in the civilian field has seen explosive growth due to their low cost, high flexibility, and wide adaptability. In order to address the drawbacks of current tethered UAV lighting, which necessitates manual operation and coordination with tethered cables, this paper presents a rapid reaction and autonomous deployment emergency lighting system prototype based on Tello UAV. The system design is divided into three modules. First, the lighting module has designed the protogype lighting extensions for the Tello UAV. By selecting and installing LED light sources reasonably, the stability of the UAV's takeoff, landing, and flight is not affected, while ensuring good lighting effects; Second, the addressing module extracts the optimization objective function based on the optimization deployment requirements of UAV, and uses simulated annealing algorithm to iteratively optimize for different distribution scenarios and user needs, calculating the optimal deployment location of UAV and planning flight missions for UAV. Third, the flight control module has designed a specialized command control framework based on Tello UAV's API, which converts the planned flight path into command statements, forms flight text, and controls the flight of unmanned aerial vehicles accordingly. The test results of the emergency lighting system show that the system can respond quickly to both densely distributed and sparsely distributed user scenarios, deploying UAV to the best service locations, and providing high-quality services to users. This work represents an early attempt of Python-based real-world UAV lighting system prototype design to associate the UAV hardware and Python algorithms.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yexin Pan, Yong Xu, Bo Ma, Chuanhuang Li. 2023-05-15. Design and Implementation of Emergency Simulated Lighting System Based on Tello UAV. https://arxiv.org/abs/2305.08542

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Constrained Feedback Control of Nonlinear Systems via Approximate HJB and Control Barrier Functions

This paper presents a two-stage framework for constrained feedback control of input-affine nonlinear systems. Offline, an approximate value function for the unconstrained problem is computed, for example using Hamilton--Jacobi--Bellman (HJB)-based policy iteration. Online, the proposed quadratic program (QP) minimizes the pre-Hamiltonian evaluated using the approximate value-function gradient subject to safety constraints enforced by control barrier functions (CBFs). This architecture decouples performance optimization from constraint enforcement, allowing constraints to be modified without recomputing the value function. As in CBF-QP architectures based on control Lyapunov functions (CLFs), safety is enforced as a hard constraint; however, the performance objective targets approximate optimality rather than a prescribed Lyapunov decay. Numerical results on a linear 2-state hovercraft and a nonlinear 9-state spacecraft attitude-control problem show agreement with the constrained open-loop optimal control problem (OCP) benchmark in the linear case, and performance close to the OCP benchmark, improving on CLF-based controllers, in the nonlinear case.

eess.SY↗
arXiv2026

Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation

We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses a Rao-Blackwellization technique to infer joint state-parameter posteriors efficiently. In particular, conditional state distributions are computed analytically via Kalman filtering, while model parameters, including the measurement-noise covariance, are approximated using particle-based Stein Variational Gradient Descent (SVGD), enabling stable real-time inference. To handle parameters subject to physical constraints, we further introduce constrained variants that enforce them through an alternating direction method of multipliers (ADMM) splitting of the SVGD update, including nonlinear equality constraints that standard particle filters cannot readily handle. We derive a stability bound that relates the approximation error in the parameter posterior to the resulting error in the marginal state distribution. Performance of the proposed filters is validated on three case studies: a fed-batch bioreactor with Haldane kinetics and a damped pendulum, both under physical constraints, and a neural-network-augmented dynamic system. The examples cover parameter estimation under inequality and equality constraints and online neural-network training within a dynamical model.

eess.SY↗
arXiv2026

Firing Rate Neural Network Implementations of Model Predictive Control

Human and animal brains perform planning to enable complex movements and behaviors, a process that can be effectively described using model predictive control (MPC). How could the brain physically implement MPC? In this work, we translate model predictive controllers into firing rate neural networks, offering insights into the nonlinear neural dynamics that underpin planning. We propose a constructive method; no training is required. This is done first applying the projected gradient method to the dual problem to derive a baseline neural network implementation. We then use factorization and contraction analysis to systematically generate alternative network architectures; in other words, we systematically generate hypotheses for how planning is done in the brain via neural dynamics. Finally, we present numerical simulations to study different neural networks performing MPC to balance an inverted pendulum on a cart (i.e., balancing a stick on a hand), including one example in which imposing sparse connectivity (a property observed in brain networks) does not degrade control performance.

eess.SY↗