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On the Performance of Jerk-Constrained Time-Optimal Trajectory Planning for Industrial Manipulators

2024-04-11 16:18:37
Jee-eun Lee, Andrew Bylard, Robert Sun, Luis Sentis


Jerk-constrained trajectories offer a wide range of advantages that collectively improve the performance of robotic systems, including increased energy efficiency, durability, and safety. In this paper, we present a novel approach to jerk-constrained time-optimal trajectory planning (TOTP), which follows a specified path while satisfying up to third-order constraints to ensure safety and smooth motion. One significant challenge in jerk-constrained TOTP is a non-convex formulation arising from the inclusion of third-order constraints. Approximating inequality constraints can be particularly challenging because the resulting solutions may violate the actual constraints. We address this problem by leveraging convexity within the proposed formulation to form conservative inequality constraints. We then obtain the desired trajectories by solving an $\boldsymbol n$-dimensional Sequential Linear Program (SLP) iteratively until convergence. Lastly, we evaluate in a real robot the performance of trajectories generated with and without jerk limits in terms of peak power, torque efficiency, and tracking capability.

Abstract (translated)

约束抖动轨迹具有改善机器人系统性能的广泛优势,包括提高能源效率、耐久性和安全性。在本文中,我们提出了一个新方法来处理约束抖动的时间最优轨迹规划(TOTP),该方法在满足多达第三维约束的同时沿着指定路径行驶,以确保安全和平滑的运动。约束抖动TOTP的一个重要挑战是包含第三维约束的非凸性陈述。近似不等式约束可能特别具有挑战性,因为 resulting 解决方案可能违反实际约束。我们通过利用约束中的凸性来形成保守的不等式约束。然后,我们通过迭代求解$\boldsymbol n$维序列线性规划(SLP)来获得所需的轨迹,直到收敛。最后,我们在实机器人上评估使用抖动限制和非抖动限制生成的轨迹在峰值功率、扭矩效率和跟踪能力方面的性能。



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