An unmanned surface vehicle berthing planning based on bacteria foraging optimization algorithm considering energy consumption constraints

Abstract Unmanned surface vehicles (USVs) frequently encounter inadequate energy levels while navigating to their destinations, which complicates their successful berthing in intricate harbor environments. A bacterial foraging optimization algorithm (BFO) is proposed that takes energy consumption into account and incorporates multiple constraints (MC-BFO). The energy consumption model is redefined for wind environments, enhancing the sensitivity of USVs to wind conditions. Additionally, a reward function is integrated into the algorithm, and the fitness function is reconstructed to improve the goal orientation of the USV. This approach enables the USV to maintain a reasonable path length while pursuing low energy consumption, resulting in more practical navigation. Constraining the USV’s sailing posture for smoother paths and restricting the USV’s heading and speed near the berthage facilitate safe berthing. Finally, three distinct experimental environments are established to compare the paths generated by MC-BFO, BFO, and genetic algorithm under both downwind and upwind conditions, ensuring consistency in relevant parameters. Data on sailing posture, energy consumption, and path length are collected, generalized, and analyzed. The results indicate that MC-BFO effectively reduces energy consumption while maintaining an acceptable path length, resulting in smoother and more coherent paths compared to traditional segmented planning. In conclusion, this method significantly enhances the quality of the berthing path.