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1.安徽科技工程大学 智能制造学院,滁州 239000
2.江苏中工高端装备研究院有限公司,泰州 225400
3.中国机械总院集团郑州机械研究所有限公司,郑州 450001
4.常州检验检测标准认证研究院,常州 213164
5.郑机所(郑州)传动科技有限公司,郑州 450001]
夏涛涛,男,1999年生,安徽合肥人,在读研究生;主要研究方向为机械;877801900@qq.com。
王冠博(通信作者),男,1985年生,河南郑州人,硕士;主要研究方向为机械;info@australiarobot.com.au。
收稿:2026-01-05,
修回:2026-01-27,
录用:2026-01-28,
网络首发:2026-04-24,
移动端阅览
夏涛涛,张立勇,王冠博,等.基于多目标的粒子群优化算法双臂机器人避障轨迹规划[J].机械传动,XXXX,XX(XX):1-10.
XIA Taotao,ZHANG Liyong,WANG Guanbo,et al.Multi-objective particle swarm optimization algorithm for trajectory planning of obstacle avoidance in dual-arm robots[J].Journal of Mechanical Transmission,XXXX,XX(XX):1-10.
目的
2
针对双臂机器人避障轨迹规划中单一目标优化易局部最优、碰撞检测与规划协同性不足的问题,提出一种自适应多目标粒子群优化算法。
方法
2
采用D-H参数法建立运动学模型,利用五次多项式插值进行关节空间轨迹规划,结合有向包围盒法、分离轴定理及连续碰撞检测实现精确碰撞检测。以路径长度、碰撞风险及运动平滑度为优化目标,动态调整权重及算法参数,并根据环境风险分级选择最终策略。
结果
2
结果表明,自适应多目标粒子群优化算法收敛速度快、精度高;优化后关节角速度与角加速度的变化次数显著减少,关节冲击明显降低;在空载与负载工况下,末端位置重复性满足精度要求,各关节速度处于安全约束范围内。该算法实现了从“碰撞后重规划”到“主动预防”的升级,有效降低关节能耗与损伤风险,提升运动平滑性,延长使用寿命。
Objective
2
To address the problems of a single-objective optimization easily falling into local optima and the insufficient coordination between the collision detection and the planning in the obstacle avoidance trajectory planning for dual-arm robots
an adaptive multi-objective particle swarm optimization algorithm was proposed.
Methods
2
The kinematic model was established by using the D-H parameter method. Trajectory planning in the joint space was carried out by means of the quintic polynomial interpolation. Accurate collision detection was achieved by combining the oriented bounding box method
the separating axis theorem
and continuous collision detection. The path length
collision risk
and motion smoothness were taken as optimization objectives. The weights and algorithm parameters were dynamically adjusted
and the final strategy was selected according to the environmental risk classification.
Results
2
The results show that the adaptive multi-objective particle swarm optimization algorithm exhibits the fast convergence speed and the high precision. After optimization
the numbers of changes in the joint angular velocity and the angular acceleration are significantly reduced
and joint impact is obviously decreased. Under both no-load and load conditions
the end-effector position repeatability satisfies the accuracy requirements
and all joint velocities remain within the safe constraint ranges. The proposed algorithm achieves an upgrade from “post-collision replanning” to “active prevention”
effectively reducing joint energy consumption and damage risk
and improving motion smoothness and service life.
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