Bearing-Constrained Leader-Follower Formation Control with Disturbance Rejection

Core Concepts

Stabilizing leader-follower formations with adaptive control laws.

Abstract

This paper explores stabilizing leader-follower formations under disturbances using adaptive variable-structure control laws. It focuses on bearing-constrained formations, providing control laws that stabilize agents to desired formations without needing full information on disturbances. The study shows that even when leaders move uniformly, the target formation can still be achieved. The proposed control laws offer a unified solution for leader-follower formation control and tracking with unknown disturbances. By utilizing bearing vectors, the research aims to reduce sensor requirements and enable decentralized/distributed control. Theoretical background on bearing rigidity theory is presented along with practical applications in robotics and unmanned systems.

Stats

A set of leaders are positioned at desired locations. Unknown uniformly bounded disturbance affects follower agents. Adaptive variable-structure formation control laws provided. Simulation results support stability analysis.

Quotes

"The proposed adaptive mechanism alters the magnitude of the control law with regard to errors of the desired and actual bearing constraints." "Using only bearing vectors reduces sensor requirements and enables decentralized/distributed control."

Key Insights Distilled From

Bearing-Constrained Leader-Follower Formation of Single-Integrators with Disturbance Rejection

by Thanh Truong... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2305.19353.pdf

Deeper Inquiries

How can these adaptive mechanisms be applied to other types of formations or systems

The adaptive mechanisms proposed in the context of bearing-constrained formation control can be applied to various other types of formations or systems. One way is to extend these adaptive strategies to formations with different geometric constraints, such as distance-based or angle-based formations. By modifying the control laws to incorporate measurements related to these constraints and adjusting the adaptive gains accordingly, similar stability guarantees and disturbance rejection properties can be achieved for a broader range of formation types. Additionally, these adaptive mechanisms can also be adapted for use in multi-agent systems beyond just formations, such as cooperative task allocation among autonomous agents or distributed sensor networks.

What are the limitations of using only bearing vectors for formation control

Using only bearing vectors for formation control has certain limitations that need to be considered. One limitation is the inability to directly account for distances between agents in the formation. This lack of distance information may lead to challenges in accurately estimating inter-agent separations and could result in suboptimal performance when dealing with complex formations or dynamic environments. Another limitation is sensitivity to measurement errors or uncertainties in bearing angles, which could impact the overall robustness and convergence properties of the control system. Furthermore, relying solely on bearing vectors may restrict the flexibility and adaptability of the control laws compared to using additional sensing modalities like distances or angles.

How does this research impact the development of autonomous systems beyond robotics

This research significantly impacts the development of autonomous systems beyond robotics by providing insights into decentralized control strategies for multi-agent coordination under unknown disturbances. The adaptive variable-structure approaches presented offer a framework for achieving stable and robust behavior in complex environments where disturbances are present but not fully known a priori. These findings have implications for applications ranging from unmanned aerial vehicles (UAVs) coordinating their flight paths autonomously based on visual cues (bearing-only measurements) to distributed sensor networks optimizing data collection tasks collaboratively while adapting dynamically to changing environmental conditions. By leveraging these adaptive mechanisms, autonomous systems can enhance their resilience, efficiency, and scalability across various domains requiring coordinated actions among multiple agents.