In autonomous navigation, trajectory replanning, refinement, and control command generation are essential for effective motion planning. This paper presents a resilient approach to trajectory replanning addressing scenarios where the initial planner's solution becomes infeasible. The proposed method incorporates a hybrid A* algorithm to generate feasible trajectories when the primary planner fails and applies a soft constraints-based smoothing technique to refine these trajectories, ensuring continuity, obstacle avoidance, and kinematic feasibility. Obstacle constraints are modelled using a dynamic Voronoi map to improve navigation through narrow passages. This approach enhances the consistency of trajectory planning, speeds up convergence, and meets real-time computational requirements. In environments with around 30\% or higher obstacle density, the ratio of free space before and after placing new obstacles, the Resilient Timed Elastic Band (RTEB) planner achieves approximately 20\% reduction in traverse distance, traverse time, and control effort compared to the Timed Elastic Band (TEB) planner and Nonlinear Model Predictive Control (NMPC) planner. These improvements demonstrate the RTEB planner's potential for application in field robotics, particularly in agricultural and industrial environments, where navigating unstructured terrain is crucial for ensuring efficiency and operational resilience.

翻译：在自主导航中，轨迹重规划、优化与控制指令生成是实现有效运动规划的关键环节。本文提出一种鲁棒的轨迹重规划方法，以应对初始规划器解不可行的场景。该方法在主要规划器失效时引入混合A*算法生成可行轨迹，并采用基于软约束的平滑技术对这些轨迹进行优化，确保连续性、避障能力与运动学可行性。通过动态Voronoi图对障碍物约束进行建模，以提升狭窄通道中的导航性能。该方法增强了轨迹规划的一致性，加速了收敛速度，并满足实时计算需求。在障碍物密度约为30%或更高的环境中（即新增障碍物前后自由空间比例），相较于定时弹性带规划器和非线性模型预测控制规划器，弹性定时弹性带规划器在遍历距离、遍历时间与控制能耗方面实现了约20%的降低。这些改进证明了弹性定时弹性带规划器在野外机器人领域的应用潜力，特别是在农业与工业环境中，其非结构化地形导航能力对保障作业效率与运行鲁棒性至关重要。