基于改进量子粒子群算法的机器人关节空间运动轨迹规划优化

Trajectory Planning in Robot Joint Space Based on Improved Quantum Particle Swarm Optimization Algorithm

机器人轨迹规划是机器人运动控制实现的关键步骤,轨迹规划的效率与精度直接关系到机器人运动控制的实时性与准确性。将机器人运动轨迹映射到关节空间,并建立轨迹规划的数学模型,使其满足运动过程中的各项物理约束,并避免各关节间的耦合问题。针对量子粒子群算法进行改进,提高其收敛速度,避免陷入局部最优,提出改进量子粒子群算法,并将之应用于机器人轨迹规划的数学模型求解。并且对基于改进量子粒子群算法的机器人关节空间轨迹规划进行测试。测试结果表明,该方法可以代替传统的机器人轨迹规划算法,并且在精度和效率方面具有更高的优势。

Trajectory planning is the key step of robot motion control.The efficiency and accuracy of trajectory planning are directly related to the real-time and accuracy of robot motion control.The trajectory of the robot is mapped to the joint space,and the mathematical model of trajectory planning is established to meet the physical constraints in the process of motion and avoid the coupling problem between the joints.In order to improve the convergence speed and avoid falling into local optimum,an improved quantum particle swarm optimization algorithm is proposed and applied to solve the mathematical model of robot trajectory planning.And the trajectory planning in robot joint space based on improved quantum particle swarm optimization algorithm is tested.The test results show that this method can replace the traditional trajectory planning algorithm,and has higher advantages in accuracy and efficiency.