Real-Time Dynamic Multiobjective Path Planning: A Case Study

Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles, as well as the realism and immersion of virtual environments. However, traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments. This study addresses these limitations through a proposed real-time dynamic multiobjective (RDMO) path-planning algorithm based on an enhanced A* framework. The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real time. Simulation experiments are conducted using real-world road network data and benchmarked against mainstream hybrid approaches based on genetic algorithms (GAs) and simulated annealing (SA). The results show that the computational speed of the RDMO algorithm is 88 and 73 times faster than that of the GA- and SA-based solutions, respectively, while the total planned path length is reduced by 58% and 33%, respectively. In addition, the RDMO algorithm also shows excellent responsiveness to dynamic changes in map connectivity and can achieve real-time replanning with a minimal computational overhead. The research results prove that the RDMO algorithm provides a robust and efficient solution for multiobjective path planning in games and robotics applications and has a great application potential in improving system performance and user experience in related fields in the future.