Simultaneous Target Tracking for Multiple Underwater Robots With Certified Safety by Using Recurrent Neural Network

The trajectory planning problem is a key aspect of underwater robot operations. Most existing studies focus on a single underwater robot and environmental obstacles, with limited research on collision avoidance planning among multiple underwater robots. This article addresses these issues for multiple underwater robots by performing simultaneous target tracking (STT) from an optimization perspective. Based on the neurodynamic design method, the path tracking, collision avoidance, and communication maintenance are formulated as a series of inequality and equality constraints. By incorporating these constraints, the STT scheme, which is depicted as a unified quadratic program (QP) framework, is thus established for multiple underwater robots with certified safety. Such a QP-type STT scheme is then solved by using the Lagrangian-based recurrent neural network (RNN). Computer simulations are performed to further verify the effectiveness and feasibility of the proposed QP-type STT scheme. These results indicate that multiple underwater robots via the proposed STT scheme can accomplish the assigned target-tracking tasks while maintaining a safe distance to avoid collision and staying within communication range throughout the tracking mission.