This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets of coefficients for adaptability to the surrounding environment. The simulation results demonstrate that the algorithm is appropriate for generating obstacle avoidance paths. The algorithm was implemented on the ROS platform using NVIDIA’s Jetson Xavier, and driving experiments were conducted with a steer-type AGV. Through measurements of computation time and real obstacle avoidance experiments, it was shown to be practical in the real world.

As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds–Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications.