In the rapidly evolving field of autonomous systems, multi-robot formations are becoming increasingly critical for defence and security applications. Researchers Chaz Cornwall and Jeremy P. Bos have developed a novel approach to formation planning that promises to enhance the effectiveness of robotic teams in complex environments. Their work addresses a fundamental challenge in multi-robot systems: the discrepancy between maintaining a predefined formation shape and minimizing the original cost function, such as protection or obstacle avoidance.
Traditionally, formation control has been used to simplify the minimization of multi-robot cost functions by encoding the cost function as a shape that the robots maintain. For instance, a diamond or box formation is often employed to protect all members of the formation. However, as more information about the surrounding environment becomes available, static shapes often fail to minimize the original cost function effectively. This mismatch can lead to suboptimal performance in critical defence scenarios.
To bridge this gap, Cornwall and Bos propose a formation planner that reduces the mismatch between a formation and the cost function while still leveraging efficient formation controllers. Their approach involves a two-step optimization problem that identifies desired relative robot positions. The first step solves a constrained problem to estimate non-linear and non-differentiable costs using a weighted sum of surrogate cost functions. The researchers theoretically analyze this problem and identify situations where weights do not need to be updated. The weighted, surrogate cost function is then minimized using relative positions between robots.
The desired relative positions are realized using a non-cooperative formation controller derived from Lyapunov’s direct approach. This method ensures that the robots maintain their optimal formation while adapting to dynamic environments. The researchers demonstrate the efficacy of this approach for military-like costs such as protection and obstacle avoidance. In simulations, they show that a formation planner can reduce a single cost by over 75%. When minimizing a variety of cost functions simultaneously, using a formation planner with adaptive weights can reduce the cost by 20-40%.
The practical applications of this research are significant for the defence and security sector. Autonomous robotic systems are increasingly being deployed in high-stakes environments where precision and adaptability are paramount. By minimizing a surrogate cost function that closely approximates the original cost function, formation planning provides better performance than relying on a static shape abstraction. This approach can enhance the effectiveness of robotic teams in missions such as surveillance, reconnaissance, and protection of critical infrastructure.
Moreover, the ability to adapt to dynamic environments and minimize multiple cost functions simultaneously makes this formation planner particularly valuable for defence applications. As the threat landscape evolves, the need for adaptive and intelligent robotic systems becomes ever more critical. The work of Cornwall and Bos represents a significant step forward in addressing these challenges, offering a robust solution that can be integrated into existing and future defence technologies. Read the original research paper here.