In context
In early 2024, as China's chemical industry expanded, firefighting robots were increasingly deployed in hazardous environments, yet most relied on remote operation and struggled with narrow sensing and poor human-robot interaction. Multi-robot coordination in complex fire scenes posed significant safety challenges, driving research into robust formation control and obstacle avoidance.
What was reported
Researchers from Beijing University of Posts and Telecommunications proposed a formation obstacle-avoidance control method combining a virtual leader-leader-follower structure with an improved artificial potential field method. The approach uses a circular motion control law and bearing-angle positioning to converge robots to desired positions on a circle centered at the virtual leader.
To address road boundary constraints, a logarithmic obstacle function creates danger zones along road edges, keeping the formation within safe areas. The resultant repulsive force from obstacles and boundaries is adjusted to be perpendicular to the attractive force, mitigating local minima and unreachable target problems. A dynamic weight factor adaptively balances formation and obstacle-avoidance controllers, improving motion smoothness.
Simulations compared the method against traditional and fixed-weight improved artificial potential field methods, showing superior convergence speed, tracking error, and obstacle avoidance. Physical experiments with three intelligent firefighting robots confirmed the controller's ability to maintain formation within feasible road areas while avoiding obstacles.
Why it mattered
This work advanced multi-robot coordination for firefighting by integrating road boundary constraints and adaptive weighting, enhancing safety and reliability in dynamic environments. It offered a practical solution for autonomous firefighting teams, reducing reliance on remote operators and improving response effectiveness in complex scenarios.
“The results show that the designed controller outperforms the other methods regarding convergence speed, tracking error, and obstacle avoidance effect.”
Source: 《机器人》期刊 (robot.sia.cn) · Published 2024-02-04 · “面向多智能消防机器人的编队避障控制方法”