In this paper, dual RRT optimization is proposed to solve the formation shape generation problem for a large number of MUVs. Since large numbers of MUVs are prone to collision during formation shape generation, this paper considers the use of path planning algorithms to solve the collision avoidance problem. Additionally, RRT as a commonly used path planning algorithm has non-optimal solutions and strong randomness. Therefore, this paper proposes a dual RRT optimization to improve the drawbacks of RRT, which is applicable to the formation shape generation of MUVs. First, an initial global path can be obtained quickly by taking advantage of RRT-connect. After that, RRT* is used to optimize the initial global path locally. After finding the section that needs to be optimized, RRT* performs a new path search on the section and replaces the original path. Due to its asymptotic optimality, the path obtained by RRT* is shorter and smoother than the initial path. Finally, the algorithm can further optimize the path results by introducing a path evaluation function to determine the results of multiple runs. The experimental results show that the dual RRT operation optimization can greatly reduce the running time while avoiding obstacles and obtaining better path results than the RRT* algorithm. Moreover, multiple runs still ensure stable path results. The formation shape generation of MUVs can be completed in the shortest time using dual RRT optimization.

Traditional formation control methods are widely used in the field of unmanned ground vehicle formation, but they lack mechanisms with which to effectively cope with complex terrains that occur during movement. In order to better improve the adaptation and coping ability of an unmanned ground vehicles (UGVs) fleet to complex terrains, this paper proposes a formation change influence factor to solve the UGVs formation and formation change problem. First, this paper adopts the leader–follower method with more flexible control to design the formation controller and derives a control law that can make the formation system stable so as to ensure that the fleet maintains the preset formation during movement. After that, this paper combines formation geometry change and dynamic adjustment to build a formation change library. The formation change influence factor is used to drive the fleet to choose the appropriate formation change strategy in the formation change library to ensure the fleet can safely pass the complex terrains. The experimental results show that, compared with the traditional formation method, the UGVs formation and change method using the formation change influence factor can flexibly and efficiently cope with various complex terrains while maintaining stability within the fleet, effectively improving the safety of the UGVs fleet and the possibility of practical application.