农业机器人末端执行器抓持力控制研究
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摘要
由于果蔬采摘作业的复杂性和特殊性,农业机器人采摘成功率低,损伤率高。引起损伤率高的一个主要原因是末端执行器抓持果蔬力过大或过小,导致抓伤或脱落。果蔬在抓持过程中会发生变形,使抓持力逐渐变小,采摘中应考虑果蔬变形对末端执行器夹持力的影响。果蔬抓取过程要求对果实无任何损伤,这就要求机器人的末端执行器具有柔顺性。本文主要从主动柔顺控制的方法,即力控制的角度出发,解决目前机械手采摘、抓取易损伤果蔬的弊端。完成的主要工作有：
1以农业流变学、机器人学、机器人操作理论为依据,通过弹簧、阻尼器和扭簧的串并联组建5元件的机械模型。当机械手指在被抓持物体的分布间距比手指的尺寸大的多时,可认为是每个手指均对应一个5元件模型,因而可利用刚体抓取理论建立果蔬的通用静态抓取模型。结合机器人收获中的水果抓取问题推导抓取映射矩阵,采用有界力封闭计算采摘水果所需的抓持条件。
2果蔬自动抓取系统试验平台的研制,包括末端执行器、控制系统硬件、软件等设计。试验平台由末端执行器、运动控制卡、数据采集卡、交流伺服电机、交流伺服驱动器、测力传感器、信号放大器、稳压电源及工控机等组成,构成以PC机为基础的开放式末端执行器抓取力控制试验系统。系统具有良好的扩展性、通用性,实用性,能够满足抓取控制要求。
3末端执行器在抓持果蔬过程中会发生变形,适合采用力和位置控制的串级控制结构。针对自行研发的抓取试验平台,提出一种适合果蔬抓取的力外环控制算法,将力偏差转换为位控系统的速度输入指令,随着力偏差的减小,末端执行器自减速停止,可减小力超调和振荡。控制系统稳定可靠,力传感器测量精度为0.3%FS,抓取西红柿成功率为96%以上,抓伤率低于10%,果蔬损伤均在高速抓取情况下出现。
4为使末端执行器和变形果蔬间的夹持力快速低超调地跟踪设定值,提出基于灰色预测的增量PI力控制算法。该算法通过采集被抓物体受到的抓持力建立灰色预测模型,当预测模型精度较高或较低时,相应地加大或减小预测力偏差在综合力偏差中的权值,使力控制器可以利用过去、当前和未来的物体抓持力信息来计算合适的控制校正量对抓持力偏差进行预补偿,可使控制器获得超调量小和响应快速的特点,对末端执行器和可变形物体之间的动态抓持过程具有适应性。果蔬抓取实验证明了灰色预测PI力控制算法的有效性,可减小果蔬抓持损伤。
5借助人类抓取果蔬的经验和知识,编制成模糊控制规则,可提高末端执行器抓取的成功率和可靠性。由于需要抓取的果蔬刚度不同,使用传统的控制并不能有效地解决抓持力控制问题,当果蔬刚度很高或很低时,根据标称环境设计的力控制器有可能失稳或响应太慢。为提高控制性能,可将模糊控制器与积分控制器结合起来构成复合控制器。积分控制器可以消除或减小稳态误差,使系统的稳态性能得到改善。积分I+模糊PD智能并行力控制算法非常适合果蔬的高速抓取。
6预测控制算法同样适合具有旋转关节的机械手抓取。通过角刚度将关节力矩和关节角度联系起来,预测关节力矩的变化有助于估算抓取果蔬的力大小,进而减小果蔬的抓取损伤。提出一种力矩外环的果蔬抓取控制算法,在实际输出关节力矩和灰色预测力矩之间实行线性插值,按采样周期将预测力矩逐渐加到力矩回路中,可减小机械手输出力矩、角速度的振幅、超调,有利于保护被抓物体不受损伤。
7农业收获机器人一般工作在高度非结构环境下,当存在过程和测量噪声的情况下,提出了基于卡尔曼滤波算法的RBF神经网络和PD复合控制方案,该方案由常规PD控制器、RBFNN前馈控制器和卡尔曼滤波器组成。当增大随机噪声的幅值,改变控制系统的采样频率时,仍然能够快速跟踪设定参考信号,具有优良的跟踪性能和较强的自适应和鲁棒性。
The success rate of fruit and vegetable picking with the agricultural robot is very low and the loss rate is very high, because of task complexity and environment particularity. A major reason of high damage rate is that grasp force on end-effector is too big or small, which makes the fruit and vegetable crack or fall off. Grasp force can turn small gradually while grasping fruit and vegetable, it is necessary to consider the deformation effect on grasp force. It was required that the end-effectors were provided with compliance for no any damage in the course of grasping fruit and vegetable. Active Compliance control, that is, force control was adopted in order to solve the damage while manipulator picking or grasping fruits and vegetables. The completed work is generalized as follows:
1. A five-component mechanical model was built with the springs, dampers, and torsional springs through series parallel of them, based on agricultural rheology, robotics, robotic manipulation. Given that the distribution distance between robot fingers and object is significantly larger than dimensions of fingers, each finger was considered to be contacted with a five-component model, thus a general static grasp model of fruit and vegetable was established by rigid grasping theory. Grasp map matrix was derived for robot grasping fruit, and the required grasp condition was calculated according to bounded force closure.
2. An experiment platform of automatic grasping system for fruit and vegetable was developed. The experiment platform was consisted of end-effector, control card, data acquisition card, AC servo motor, AC servo driver, force sensor, signal amplifier, regulated power, industrial computer, et al, so the open architecture force control system of end-effector was built up based on PC. The grasp system has good expansibility, generality, practicability, and force control precision about end-effector can be satisfied.
3. Deformation can occur while end-effector grasping fruit and vegetable, so series control with force/position control was adopted in the control system frame. The external force control loops algorithm was proposed for the control system in experiment, and force error was converted to speed input of position control system. The end-effector speed adaptive decelerated as force error decreasing in order to reduce force overshoots. The grasp control system is steady, the measuring precision of force sensors is0.3%FS, the success rate of grasping tomatoes is beyond96%, and the damage rate is less than10%, and all the damages appear in high speed grasping.
4. Incremental PI force control algorithm was proposed based on grey prediction in order to make grasp force track set value quickly without big overshoot. Grey prediction model was built by force data acquisition from the grasped objects, the weights of predictive force error were increased or decreased in integrated error accordingly to that the precision of predictive model is high or low. Force controller can employ the past, present and future grasp force information to calculate an appropriate control correction to pre-compensate the force error, and can yield small overshot, fast response simultaneously, which make the controller adaptive to the dynamic grasp process between deformable objects and end-effector. Experimental results are presented to demonstrate the efficacy of grey predictive incremental PI force control algorithm, which can reduce the damage rate in grasping.
5. If experience and knowledge about human grasping fruit and vegetable can be translated into control rules, successful and safe grasping of end-effectors would be increased evidently. Conventional control can't solve force control satisfactorily because stiffness of fruits and vegetables is different. The force controller designed with standard condition may be instability when stiffness of fruits and vegetables is quite high or low. Composite controller consisted of fuzzy controller and integral controller was used to improve control performance. Integral controller could eliminate or reduce steady-state error, and improved the steady performance. A concurrent control force algorithm was proposed and tested as a good method to grasp fruit and vegetable.
6. Prediction control algorithm is suitable for manipulator grasping with rotary joint, too. The relation between joint torque and joint angle was built through angular stiffness, the change of joint torque was predicted to help to know grasping force information to decrease the loss when grasping fruit and vegetable. Grasping control schemes was proposed based on external torque control loops, and the future joint torque would be gradually added to torque loops according to sampling period based on linear interpolation between the current torque and predictive one. Force overshoot and oscillation about output torque and angular velocity of manipulator would be depressed, made the grasped objects free from damage.
7. Agriculture harvesting robot always works under high non-normal environment. In order to suppress the effect of process and measurement to control system, a RBF neural network and PD combined controller was proposed based on the Kalman filter, the controller was composed of RBF neural network forward feed controller, PD feedback controller and Kalman filter. It always could fast track the fixed value when the noise amplitude was enlarged or sampling frequency of control system was changed, which has excellent tracking performance and quite adaptive and robust.
1以农业流变学、机器人学、机器人操作理论为依据,通过弹簧、阻尼器和扭簧的串并联组建5元件的机械模型。当机械手指在被抓持物体的分布间距比手指的尺寸大的多时,可认为是每个手指均对应一个5元件模型,因而可利用刚体抓取理论建立果蔬的通用静态抓取模型。结合机器人收获中的水果抓取问题推导抓取映射矩阵,采用有界力封闭计算采摘水果所需的抓持条件。
2果蔬自动抓取系统试验平台的研制,包括末端执行器、控制系统硬件、软件等设计。试验平台由末端执行器、运动控制卡、数据采集卡、交流伺服电机、交流伺服驱动器、测力传感器、信号放大器、稳压电源及工控机等组成,构成以PC机为基础的开放式末端执行器抓取力控制试验系统。系统具有良好的扩展性、通用性,实用性,能够满足抓取控制要求。
3末端执行器在抓持果蔬过程中会发生变形,适合采用力和位置控制的串级控制结构。针对自行研发的抓取试验平台,提出一种适合果蔬抓取的力外环控制算法,将力偏差转换为位控系统的速度输入指令,随着力偏差的减小,末端执行器自减速停止,可减小力超调和振荡。控制系统稳定可靠,力传感器测量精度为0.3%FS,抓取西红柿成功率为96%以上,抓伤率低于10%,果蔬损伤均在高速抓取情况下出现。
4为使末端执行器和变形果蔬间的夹持力快速低超调地跟踪设定值,提出基于灰色预测的增量PI力控制算法。该算法通过采集被抓物体受到的抓持力建立灰色预测模型,当预测模型精度较高或较低时,相应地加大或减小预测力偏差在综合力偏差中的权值,使力控制器可以利用过去、当前和未来的物体抓持力信息来计算合适的控制校正量对抓持力偏差进行预补偿,可使控制器获得超调量小和响应快速的特点,对末端执行器和可变形物体之间的动态抓持过程具有适应性。果蔬抓取实验证明了灰色预测PI力控制算法的有效性,可减小果蔬抓持损伤。
5借助人类抓取果蔬的经验和知识,编制成模糊控制规则,可提高末端执行器抓取的成功率和可靠性。由于需要抓取的果蔬刚度不同,使用传统的控制并不能有效地解决抓持力控制问题,当果蔬刚度很高或很低时,根据标称环境设计的力控制器有可能失稳或响应太慢。为提高控制性能,可将模糊控制器与积分控制器结合起来构成复合控制器。积分控制器可以消除或减小稳态误差,使系统的稳态性能得到改善。积分I+模糊PD智能并行力控制算法非常适合果蔬的高速抓取。
6预测控制算法同样适合具有旋转关节的机械手抓取。通过角刚度将关节力矩和关节角度联系起来,预测关节力矩的变化有助于估算抓取果蔬的力大小,进而减小果蔬的抓取损伤。提出一种力矩外环的果蔬抓取控制算法,在实际输出关节力矩和灰色预测力矩之间实行线性插值,按采样周期将预测力矩逐渐加到力矩回路中,可减小机械手输出力矩、角速度的振幅、超调,有利于保护被抓物体不受损伤。
7农业收获机器人一般工作在高度非结构环境下,当存在过程和测量噪声的情况下,提出了基于卡尔曼滤波算法的RBF神经网络和PD复合控制方案,该方案由常规PD控制器、RBFNN前馈控制器和卡尔曼滤波器组成。当增大随机噪声的幅值,改变控制系统的采样频率时,仍然能够快速跟踪设定参考信号,具有优良的跟踪性能和较强的自适应和鲁棒性。
The success rate of fruit and vegetable picking with the agricultural robot is very low and the loss rate is very high, because of task complexity and environment particularity. A major reason of high damage rate is that grasp force on end-effector is too big or small, which makes the fruit and vegetable crack or fall off. Grasp force can turn small gradually while grasping fruit and vegetable, it is necessary to consider the deformation effect on grasp force. It was required that the end-effectors were provided with compliance for no any damage in the course of grasping fruit and vegetable. Active Compliance control, that is, force control was adopted in order to solve the damage while manipulator picking or grasping fruits and vegetables. The completed work is generalized as follows:
1. A five-component mechanical model was built with the springs, dampers, and torsional springs through series parallel of them, based on agricultural rheology, robotics, robotic manipulation. Given that the distribution distance between robot fingers and object is significantly larger than dimensions of fingers, each finger was considered to be contacted with a five-component model, thus a general static grasp model of fruit and vegetable was established by rigid grasping theory. Grasp map matrix was derived for robot grasping fruit, and the required grasp condition was calculated according to bounded force closure.
2. An experiment platform of automatic grasping system for fruit and vegetable was developed. The experiment platform was consisted of end-effector, control card, data acquisition card, AC servo motor, AC servo driver, force sensor, signal amplifier, regulated power, industrial computer, et al, so the open architecture force control system of end-effector was built up based on PC. The grasp system has good expansibility, generality, practicability, and force control precision about end-effector can be satisfied.
3. Deformation can occur while end-effector grasping fruit and vegetable, so series control with force/position control was adopted in the control system frame. The external force control loops algorithm was proposed for the control system in experiment, and force error was converted to speed input of position control system. The end-effector speed adaptive decelerated as force error decreasing in order to reduce force overshoots. The grasp control system is steady, the measuring precision of force sensors is0.3%FS, the success rate of grasping tomatoes is beyond96%, and the damage rate is less than10%, and all the damages appear in high speed grasping.
4. Incremental PI force control algorithm was proposed based on grey prediction in order to make grasp force track set value quickly without big overshoot. Grey prediction model was built by force data acquisition from the grasped objects, the weights of predictive force error were increased or decreased in integrated error accordingly to that the precision of predictive model is high or low. Force controller can employ the past, present and future grasp force information to calculate an appropriate control correction to pre-compensate the force error, and can yield small overshot, fast response simultaneously, which make the controller adaptive to the dynamic grasp process between deformable objects and end-effector. Experimental results are presented to demonstrate the efficacy of grey predictive incremental PI force control algorithm, which can reduce the damage rate in grasping.
5. If experience and knowledge about human grasping fruit and vegetable can be translated into control rules, successful and safe grasping of end-effectors would be increased evidently. Conventional control can't solve force control satisfactorily because stiffness of fruits and vegetables is different. The force controller designed with standard condition may be instability when stiffness of fruits and vegetables is quite high or low. Composite controller consisted of fuzzy controller and integral controller was used to improve control performance. Integral controller could eliminate or reduce steady-state error, and improved the steady performance. A concurrent control force algorithm was proposed and tested as a good method to grasp fruit and vegetable.
6. Prediction control algorithm is suitable for manipulator grasping with rotary joint, too. The relation between joint torque and joint angle was built through angular stiffness, the change of joint torque was predicted to help to know grasping force information to decrease the loss when grasping fruit and vegetable. Grasping control schemes was proposed based on external torque control loops, and the future joint torque would be gradually added to torque loops according to sampling period based on linear interpolation between the current torque and predictive one. Force overshoot and oscillation about output torque and angular velocity of manipulator would be depressed, made the grasped objects free from damage.
7. Agriculture harvesting robot always works under high non-normal environment. In order to suppress the effect of process and measurement to control system, a RBF neural network and PD combined controller was proposed based on the Kalman filter, the controller was composed of RBF neural network forward feed controller, PD feedback controller and Kalman filter. It always could fast track the fixed value when the noise amplitude was enlarged or sampling frequency of control system was changed, which has excellent tracking performance and quite adaptive and robust.
引文
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