Fault-Tolerant Coverage Control for an Autonomous Aerial Agent with Non-Gaussian Disturbances

Core Concepts

This work proposes a hierarchical fault-tolerant coverage control approach for an autonomous aerial agent that can accommodate non-Gaussian disturbances affecting its control inputs.

Abstract

The content presents a hierarchical fault-tolerant coverage control framework for an autonomous aerial agent operating in a 3D environment. The first stage of the controller generates an ideal reference coverage plan by optimizing the agent's mobility and camera control inputs to maximize the coverage of predefined points of interest. This is formulated as a mixed-integer quadratic program. The second stage of the controller then aims to robustly guide the agent along the reference plan, even in the presence of erroneous control inputs caused by non-Gaussian disturbances. This is achieved by employing exact uncertainty propagation techniques based on mixed-trigonometric-polynomial moment computations. The controller imposes deterministic constraints on the moments of the agent's uncertain states to ensure fault-tolerant coverage at a specified probability level. The proposed approach is demonstrated through simulations, showing the agent's ability to reliably reach and cover the points of interest despite the presence of non-Gaussian disturbances on the control inputs.

Stats

The agent's horizontal and vertical velocities are set as uν t ∈ [0, 10] m/s and uz t ∈ [-10, 10] m/s, respectively. The yaw rate is uψ t ∈ [-π, π] rad/s, and the sampling interval is Δt = 0.1 s. The disturbance components ων t , ωz t , and ωψ t follow a Beta, Gaussian, and Uniform distribution, respectively.

Quotes

"To address these limitations, we propose in this work a fault-tolerant hierarchical coverage controller. It accounts for stochastic non-Gaussian disturbances affecting the nominal control inputs of an autonomous UAV agent." "The proposed approach allows for the accommodation of these disturbances, thereby facilitating the generation of fault-tolerant coverage plans."

Key Insights Distilled From

Hierarchical Fault-Tolerant Coverage Control for an Autonomous Aerial Agent

by Savvas Papai... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09838.pdf

Deeper Inquiries

How could the proposed framework be extended to handle multiple autonomous agents cooperatively covering the points of interest

To extend the proposed framework to handle multiple autonomous agents cooperatively covering points of interest, a few modifications and enhancements can be made. Firstly, the coverage planning algorithm can be adapted to allocate specific regions of interest to each agent based on their proximity or capabilities. This can involve partitioning the points of interest among the agents to ensure efficient coverage without redundancy. Additionally, communication protocols can be implemented to enable coordination between agents, allowing them to share information about their coverage plans, current positions, and any obstacles encountered. By incorporating collaborative decision-making algorithms, such as consensus algorithms or distributed optimization techniques, the agents can work together to optimize their trajectories and maximize overall coverage efficiency. Furthermore, the framework can be enhanced to include collision avoidance mechanisms and dynamic replanning strategies to ensure safe and effective cooperation among multiple agents in complex environments.

What are some potential limitations or drawbacks of the moment-based uncertainty propagation approach used in the second stage of the controller

While the moment-based uncertainty propagation approach used in the second stage of the controller offers several advantages, such as the ability to handle non-Gaussian disturbances and provide robust fault-tolerant control, there are also potential limitations and drawbacks to consider. One limitation is the computational complexity associated with computing higher-order moments, especially for systems with a large number of states or in high-dimensional spaces. This can lead to increased computational burden and longer processing times, which may not be suitable for real-time applications or systems with strict timing constraints. Additionally, the accuracy of moment-based uncertainty propagation relies on the assumption of known probability distributions for the disturbances, which may not always hold true in practical scenarios. Uncertainties in the estimation of moments or inaccuracies in the assumed distributions can impact the reliability and effectiveness of the fault-tolerant control strategy.

How could the coverage planning be further optimized to account for energy consumption, flight time, or other mission-specific objectives beyond just maximizing coverage

To further optimize coverage planning to account for energy consumption, flight time, and other mission-specific objectives beyond maximizing coverage, the framework can be enhanced with additional optimization criteria and constraints. One approach is to incorporate energy models and constraints into the optimization algorithm to minimize energy consumption while achieving the desired coverage objectives. This can involve optimizing the agents' trajectories to minimize energy expenditure, considering factors such as altitude changes, speed variations, and sensor usage. Flight time optimization can be addressed by including constraints on the agents' battery levels and charging requirements, ensuring that the coverage mission can be completed within the available flight time. Mission-specific objectives, such as prioritizing coverage of critical areas or optimizing the sequence of coverage tasks based on importance or urgency, can be integrated into the planning algorithm as additional optimization goals. By balancing coverage objectives with energy efficiency, flight time constraints, and mission priorities, the framework can provide a more comprehensive and tailored solution for autonomous aerial coverage missions.