Highly constrained trajectory optimization problems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several objectives. Because of the discontinuity or nonlinearity in the vehicle dynamics and mission objectives, it is challenging to generate a compromised trajectory that can satisfy constraints and optimize objectives. To address the multiobjective trajectory planning problem, this paper applies a specific multiple-shooting discretization technique with the newest NSGA-III optimization algorithm and constructs a new evolutionary optimal control solver. In addition, three constraint handling algorithms are incorporated in this evolutionary optimal control framework. The performance of using different constraint handling strategies is detailed and analyzed. The proposed approach is compared with other well-developed multiobjective techniques. Experimental studies demonstrate that the present method can outperform other evolutionary-based solvers investigated in this paper with respect to convergence ability and distribution of the Pareto-optimal solutions. Therefore, the present evolutionary optimal control solver is more attractive and can offer an alternative for optimizing multiobjective continuous-time trajectory optimization problems.

中文翻译：

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通过扩展进化算法解决多目标约束轨迹优化问题


高度约束的轨迹优化问题通常很难解决。由于一些现实世界的要求，可能需要制定包含多个目标的典型轨迹优化模型。由于车辆动力学和任务目标的不连续性或非线性，生成可以满足约束和优化目标的折衷轨迹具有挑战性。为了解决多目标轨迹规划问题，本文应用特定的多射击离散化技术和最新的NSGA-III优化算法，构建了一种新的进化最优控制求解器。此外，该进化最优控制框架中还结合了三种约束处理算法。详细分析了使用不同约束处理策略的性能。所提出的方法与其他成熟的多目标技术进行了比较。实验研究表明，本方法在收敛能力和帕累托最优解的分布方面优于本文研究的其他基于进化的求解器。因此，目前的进化最优控制求解器更具吸引力，可以为优化多目标连续时间轨迹优化问题提供一种替代方案。