As the core technology for realizing autonomous navigation of unmanned ground vehicles, the path planning algorithm directly determines the reliability and stability of navigation tasks in complex dynamic environments. With the expanding range of application scenarios, traditional path planning approaches have become increasingly inadequate in terms of real-time performance, dynamic obstacle avoidance, and multi-objective optimization. The recent rise in AI-based methods has provided new opportunities for this field. This paper systematically analyzes the latest research progress in this area. By reviewing and analyzing the highly recognized literature in recent years, we classify mainstream path planning and related algorithms into six types: graph-search-based, sampling-based, local optimization-based, meta-heuristic optimization, AI-based, and optimal control methods. The core improvement trends, advantages, and inherent limitations of each algorithm type are deeply analyzed. Through bibliometric analysis, we identify major gaps in current research, including over-reliance on simulation methods, overly restrictive environmental assumptions, and insufficient handling of multiple objectives. Finally, we point out the critical gap between simulation environments and real-world deployment and advocate the use of hybrid algorithms to address the deficiencies of single algorithms, along with effective validation in real environments. This direction is crucial for promoting the broader practical application of unmanned ground vehicle technology.

Successive and pulsing loading methods were employed to evaluate the elastocaloric effects (eCEs) performance of the Fe14.6Ni(at%) alloy at 293 K by molecular dynamics (MD) simulation. The study included investigation of the loading methods and the cyclic stability of eCEs performance. The study of the loading methods indicated that the pulse cyclic loading method, compared with the successive loop loading method, was the optimal option due to the steady evolution processes of the parameters. Finally, the research on cyclic stability demonstrated that the Fe14.6Ni(at%) alloy remained sensitive to the external stimulus even after 80,414 cycles. This work proposes a promising strategy for optimizing the eCEs performance of the Fe14.6Ni(at%) alloy and demonstrates its great industrial potential.