Recent advancements in multi-agent systems have sparked researchers’ interest in formation tracking control for diverse Mobile Robots in dynamic situations. Specifically, researchers employ techniques such as decentralized model predictive control and distributed observer-based designs using relative bearing measurements.
However, researchers lack adequate solutions to the finite-time convergence problem that ensure target structure formation within a set time, without considering initial conditions. Consequently, current methods often allow only asymptotic convergence, which fails in time-sensitive situations. Furthermore, practitioners find it challenging to apply collision avoidance in finite-time formation control for diverse Mobile Robots.
Researchers have studied several methods for collision avoidance in formation control, mainly using potential field approaches to create repulsive forces. While effective, these methods can harm formation performance. Control-method barrier functions help with collision avoidance but pose challenges for finite-time control in diverse mobile robot tracking.
For barrier function-based controllers to achieve stability when incorporating bearing-only measurements and robot dynamics, it is crucial to account for system disturbances and uncertainties. Additionally, researchers need to thoroughly investigate the computational complexity to ensure that the controllers can be implemented in real-time on mobile robots while considering practical constraints and operational efficiency.
Bearing-only measurements pose significant challenges in various aspects of robotic systems. Finite-time convergence, collision avoidance, and navigating heterogeneous environments become more complex due to the limitations of bearing-only data. Furthermore, the absence of range data introduces additional difficulties such as non-convexity and observability problems, necessitating the implementation of innovative control strategies for mobile robots to effectively perform safe and efficient formation tracking tasks.
It is imperative for upcoming investigators to strive for the development of robust and highly optimized algorithms capable of effectively managing uncertainties encountered in bearing measurements, robot dynamics, and intricate environmental settings. Additionally, it is vital for developers to pioneer the design and implementation of formation tracking controllers that prioritize safety in overseeing the seamless operation of multifaceted multi-mobile robot systems.
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