Motion Planning of Differentially Flat Planar Underactuated Robots

Intelligent robots are often used to explore various areas instead of humans. However, when the driving joint is damaged, the actuated robot degenerates to an underactuated robot, and the traditional control method is not suitable for the underactuated robot. In this work, a two-stage control approach for a planar prismatic–rotational (PR) underactuated robot is introduced. Firstly, we establish the dynamic model and describe the underactuated constraint between an underactuated rotational joint and active prismatic joint. Secondly, the trajectory with multiple parameters is planned to ensure that the two joints reach the target position. Based on underactuated constraints and the evaluation function, the differential evolution algorithm (DEA) is used to optimize these parameters. After that, in stage 1, we design the controller to move the active prismatic joint to the desired position. Meanwhile, the underactuated rotational joint is rotating freely. In stage 2, we design the controller for the active prismatic joint to track the planned trajectory. By means of this strategy, both joints reach their target locations simultaneously. The final simulation result demonstrates that this strategy is effective.

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Industrial Robot Trajectory Optimization Based on Improved Sparrow Search Algorithm

Ma,

Sun,

Jiang

et al. 2024

Machines

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This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward and inverse kinematic models of the robot. Subsequently, a 3-5-3 mixed polynomial interpolation trajectory planning approach is applied to the robot. Building upon the conventional sparrow algorithm, a two-dimensional Logistic chaotic system initializes the population. Additionally, a Levy flight strategy and nonlinear adaptive weighting are introduced to refine the discoverer position update operator, while an inverse learning strategy enhances the vigilante position update operator. These modifications boost both the local and global search capabilities of the algorithm. The improved sparrow algorithm, based on 3-5-3 hybrid polynomial trajectory planning, is then used for the time-optimal trajectory planning of the robot. This is compared with traditional sparrow search algorithm and particle swarm algorithm optimization results. The findings indicate that the proposed enhanced sparrow search algorithm outperforms both the standard sparrow algorithm and the particle swarm algorithm in terms of convergence speed and accuracy for robot trajectory optimization. This can lead to the increased work efficiency and performance of the robot.

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Motion Planning of Differentially Flat Planar Underactuated Robots

Cited by 4 publications

References 28 publications

Differentially Flat Robots with Compliance in Actuated Joints

Differentially Flat Robots with Compliance in Actuated Joints

Two-Stage Control Strategy Based on Motion Planning for Planar Prismatic–Rotational Underactuated Robot

Industrial Robot Trajectory Optimization Based on Improved Sparrow Search Algorithm

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