An immense body of research has focused on path-planning and following of wheeled robots in unstructured surfaces. Nonholonomic robots traveling over deformable terrains together with complex operating conditions, however, pose further challenges in terms of a higher demand for robustness and optimality. In this paper, a Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm is employed for planning an optimal path of a wheeled robot, so as to ensure shortest path from the starting point to the target location together with safety through guaranteed avoidance of collisions with static and dynamic obstacles. The fundamental terramechanics concepts are employed to derive essential forces and moments acting on the wheeled robot. Subsequently, a kineto-dynamic model of the robot is developed for designing a novel robust control algorithm based on an exponential-integral-sliding mode (EISMC) scheme and a RBF-NN approximator. The results revealed that the proposed algorithm is responsive and robust to withstand adverse effects of structured and unstructured uncertainties by using the designed adaptation law according to the Lyapunov stability theorem. The effectiveness of the proposed algorithm is also validated against several reported frameworks.

大量的研究集中在非结构化表面上轮式机器人的路径规划和跟踪上。然而，非完整机器人在复杂的操作条件下在可变形地形上行驶，在对鲁棒性和优化性的更高要求方面提出了进一步的挑战。在本文中，采用混沌增强加速粒子群优化（CAPSO）算法规划轮式机器人的最佳路径，通过保证避免碰撞来确保从起点到目标位置的最短路径以及安全性。有静态和动态障碍。基本的地形力学概念用于推导出作用在轮式机器人上的基本力和力矩。随后，开发了机器人的运动动力学模型，用于设计基于指数积分滑动模式 (EISMC) 方案和 RBF-NN 逼近器的新型鲁棒控制算法。结果表明，所提出的算法通过使用根据李雅普诺夫稳定性定理设计的适应律，具有响应性和鲁棒性，能够承受结构化和非结构化不确定性的不利影响。所提出算法的有效性也针对几个报告的框架进行了验证。