Four-wheel Independent Steering (4WIS) vehicles have attracted increasing attention for their superior maneuverability. Human drivers typically choose to cross or drive over the low-profile obstacles (e.g., plastic bags) to efficiently navigate through narrow spaces, while existing planners neglect obstacle attributes, causing inefficiency or path-finding failures. To address this, we propose a trajectory planning framework integrating the 4WIS hybrid A* and Optimal Control Problem (OCP), in which the hybrid A* provides an initial path to enhance the OCP solution. Specifically, a multimodal classification network is introduced to assess scene complexity (hard/easy task) by fusing image and vehicle state data. For hard tasks, guided points are set to decompose complex tasks into local subtasks, improving the search efficiency of 4WIS hybrid A*. The multiple steering modes of 4WIS vehicles (Ackermann, diagonal, and zero-turn) are also incorporated into node expansion and heuristic designs. Moreover, a hierarchical obstacle handling strategy is designed to guide the node expansion considering obstacle attributes, i.e., 'non-traversable', 'crossable', and 'drive-over' obstacles. It allows crossing or driving over obstacles instead of the 'avoid-only' strategy, greatly enhancing success rates of pathfinding. We also design a logical constraint for the 'drive-over' obstacle by limiting its velocity to ensure safety. Furthermore, to address dynamic obstacles with motion uncertainty, we introduce a probabilistic risk field model, constructing risk-aware driving corridors that serve as linear collision constraints in OCP. Experimental results demonstrate the proposed framework's effectiveness in generating safe, efficient, and smooth trajectories for 4WIS vehicles, especially in constrained environments.

翻译：四轮独立转向（4WIS）车辆因其卓越的机动性而受到越来越多的关注。人类驾驶员在狭窄空间内高效通行时，通常会选择跨越或碾过低矮障碍物（如塑料袋），而现有规划器忽略障碍物属性，导致效率低下或路径搜索失败。为此，我们提出了一种集成4WIS混合A*与最优控制问题（OCP）的轨迹规划框架，其中混合A*提供初始路径以提升OCP求解效果。具体而言，通过融合图像与车辆状态数据，引入多模态分类网络评估场景复杂度（困难/简单任务）。针对困难任务，设置引导点将复杂任务分解为局部子任务，以提高4WIS混合A*的搜索效率。同时将4WIS车辆的多种转向模式（阿克曼转向、对角线转向及零半径转向）纳入节点扩展与启发式设计。此外，设计了一种考虑障碍物属性的分层障碍物处理策略，即"不可通行"、"可跨越"与"可碾过"障碍物，以指导节点扩展。该策略允许跨越或碾过障碍物，而非仅采用"避让"策略，从而显著提升路径搜索成功率。针对"可碾过"障碍物，通过限制车速设计了逻辑约束以确保安全。进一步地，为处理具有运动不确定性的动态障碍物，引入概率风险场模型，构建风险感知驾驶走廊，并将其作为OCP中的线性碰撞约束。实验结果表明，所提框架能够为4WIS车辆生成安全、高效且平滑的轨迹，尤其在受限环境中表现优异。