Purpose This review paper aims to provide a comprehensive review of Dubins robot path planning, which has become increasingly important in mobile robotics, autonomous vehicles and industrial applications. The authors identify key developments, analyze existing approaches and highlight the fundamental constraints, algorithms and future opportunities involving curvature-constrained, forward-only path planning. Design/methodology/approach The review consists of three parts: basic Dubins theory, seven decades of development and future opportunities. In the first part, the authors summarize problem formulations and representative computational strategies/algorithms. In the second part, the seventy years of development are divided into four stages, with each stage summarizing developments in important topics such as point-to-point planning, obstacle avoidance and variable speed planning. In addition, the authors analyze current challenges and evaluate recent extensions in cooperative, real-time and learning-based planning frameworks. Findings Dubins path planning has evolved from simple analytical models to complex multirobot and dynamic scenarios. While exact solutions exist for basic tasks, the scalability, obstacle constraints and real-time responsiveness remain active challenges. Promising future directions include integrating machine learning for adaptive planning, and extending to Reeds–Shepp and nonholonomic systems in three-dimensional environments. Originality/value This review offers a structured and in-depth overview of path planning for Dubins robots, connecting core theoretical concepts with the latest developments and real-world implementations. Unlike prior surveys that roughly treat general nonholonomic planning or sampling-based methods, this paper for the first time emphasizes the curvature-constrained planning, especially under the Dubins constraints. By identifying underexplored areas such as dynamic environments, real-time applications, this review offers actionable insights and future research directions valuable to both researchers and practitioners in mobile and industrial robots.
Zhou et al. (Fri,) studied this question.