In this study, a new convex optimization (CO) approach to time-optimal trajectory planning (TOTP) is described, which considers both torque and jerk limits. The key insight of the approach is that the non-convex jerk limits are transformed to linear acceleration constraints and indirectly introduced into CO as the linear acceleration constraints. In this way, the convexity of CO will not be destroyed and the number of optimization variables will not increase, which give the approach a fast computation speed. The proposed approach is implemented on random geometric path of a 6-DOF manipulator. Compared with a similar method, the results show that the torque and jerk limits are addressed by a reasonable increase in the computation time. In addition, the maximum value of joint jerk reduces by over 80% and the joint torque curves are smoother in the comparison, which demonstrates that this approach has the ability to effectively restrain acceleration mutation.

在这项研究中，描述了一种新的凸优化（CO）方法用于时间最优轨迹规划（TOTP），该方法同时考虑了扭矩和加速度限制。该方法的关键见解在于，将非凸面加速度限制转换为线性加速度约束，并间接将其作为线性加速度约束引入CO。这样，不会破坏CO的凸度，并且不会增加优化变量的数量，从而使该方法具有较快的计算速度。所提出的方法是在6自由度机械臂的随机几何路径上实现的。与类似方法相比，结果表明，通过合理增加计算时间可以解决转矩和加速度限制。此外，