Optimization of layout and path planning of surgical robotic system

Positioning a surgical robot for optimal operation in a crowded operating room is a challenging task. In the robotic-assisted surgical procedures, the surgical robot’s end-effector must reach the patient’s anatomical targets because repositioning of the patient or surgical robot requires additional time and labor. This paper proposes an optimization algorithm to determine the best layout of the operating room, combined with kinematics criteria and optical constraints applied to the surgical assistant robot system. A new method is also developed for trajectory of robot’s end-effector for path planning of the robot motion. The average deviations obtained from repeatability tests for surgical robot’s layout optimization were 1.4 and 4.2 mm for x and y coordinates, respectively. The results of this study show that the proposed optimization method successfully solves the placement problem and path planning of surgical robotic system in operating room.

     

基于混合遗传算法的工业机器人最优轨迹规划

为兼顾工业机器人工作效率与轨迹的平稳性,提出一种基于混合遗传算法的二次轨迹规划方案.通过最优时间轨迹规划得到最小执行时间,在最小执行时间内进行最优冲击轨迹规划,进而规划出一条既高效又平滑的运动轨迹.采用五次均匀B样条在关节空间进行快速插值,不仅保证了各关节速度和加速度连续性还保证了各关节冲击的连续性.连续平滑的冲击可以减少机械振动,延长机器人的工作寿命.选用PUMA560为对象进行仿真与实验,结果表明,该方案可以获得比较理想的机器人运动轨迹,所提出的混合遗传算法能有效提高全局寻优的性能和算法运行的稳定性.     

执行器约束下基于数据驱动的参数化前馈控制器设计

针对执行器约束下非重复性点到点运动的轨迹跟踪问题, 提出了一种在执行器约束下基于数据驱动的参数化输入整形滤波器和前馈控制器优化设计算法. 首先对输入整形滤波器以及前馈控制器进行参数化, 然后在目标函数中加入控制信号变化量与控制信号能量的约束, 再采用基于数据驱动的迭代寻优算法得到最优参数, 在该参数下可以实现满足执行器约束条件下的运动控制系统轨迹最优跟踪性能. 并且由于采用了前馈参数化设计方法, 在点到点轨迹发生变化时所提出算法依然能够保持良好的轨迹跟踪性能. 仿真与实验结果表明在执行器约束下所提出算法能够实现最优点到点轨迹跟踪性能, 并且对非重复性点到点轨迹跟踪具有一定的鲁棒性.     

基于QPSO算法的机器人时间最优轨迹规划

在考虑关节约束的前提下,为得到工业机器人时间最优的关节运动轨迹,提出一种工业机器人时间最优轨迹规划新算法。采用五次非均匀B样条插值法构造各关节运动轨迹,得到的机器人各关节位置准确,各关节速度、加速度和加加速度曲线连续。利用量子行为粒子群优化算法(Quantum-behaved Particle Swarm Optimization,简称QPSO)进行时间最优的轨迹规划,该算法可以在整个可行域上搜索,具有较强的全局搜索能力。与标准粒子群算法(Particle Swarm Optimization,简称PSO)和差分进化算法(Differential Evolution Algorithm,简称DE)相比较,结果显示使用该算法进行时间最优的轨迹规划得到的数值结果更小。     

基于GA优化RBF神经网络的机器人轨迹规划

针对机器人在不确定环境下末端执行器运动轨迹的准确性及平稳性问题,采用基于遗传算法(GA)优化径向基函数(RBF)神经网络的轨迹规划方法对Kinova Mico2机器人进行轨迹规划研究。介绍了机器人的相关参数及坐标系、建立了D-H矩阵和运动学模型。提取机器人实际抓取物品的直线轨迹并等分插补,用GA优化并实时在线更新RBF神经网络的权值,以更优的权值参数建立新的RBF网络。研究结果表明:相比优化前,基于GA优化RBF的规划轨迹逼近误差小且平滑稳定,仿真结果较为稳定,轨迹规划的可行性满足机器人实际抓取工作的需要。     

基于运动控制和频域分析的移动机器人能耗最优轨迹规划

本文针对两轮自平衡可移动机器人, 提出了一种新的能耗最优运动轨迹规划方法.本文将轨迹规划与由轨迹跟踪控制器和机器人动力学方程组成的运动控制模型相结合, 基于期望轨迹与实际电机输入电压间的传递函数和能量在时域和频域上的对应关系, 通过频域分析的方法得到了具有明确机理表达的线性能耗模型, 并采用最小二乘线性回归法对模型参数进行辨识.对于能耗最优轨迹, 由全局路径规划得到的路径点作为局部轨迹规划的局部目标点, 通过一定的数学转换和参数求导, 可直接得到相邻两个局部目标点间的能耗最优运行轨迹和对应的运行时间.通过仿真实验证明了本文所提能耗模型的准确性和所得轨迹的能耗最优性.     

安全平滑的改进时间弹性带轨迹规划算法

传统TEB(time elastic band)算法在杂乱场景下规划易出现倒退、大转向等异常行为,造成加速度跳变,控制指令不平滑,机器人受到大冲击,不利于移动机器人轨迹跟踪.鉴于此,提出一种改进TEB算法,通过增加危险惩罚因子约束规划更安全的运动轨迹,增加加速度跳变以抑制约束减小运动中的最大冲击,增加末端平滑约束以减小末端冲击,实现目标点平滑、准确到达.构建图优化问题,以机器人的位姿和时间间隔为节点、目标函数和约束函数为边,利用问题的稀疏性快速获得相应时刻点的控制量.最后,通过基于机器人操作系统的大量对比仿真测试以及真实差速机器人上的物理实验对提出的改进TEB算法进行性能验证.结果表明,改进TEB算法在复杂环境中能够规划出更安全、平滑的轨迹,减小机器人所受冲击,实现移动机器人更合理地运动.     

基于QPSO 算法移动机器人轨迹规划与实验

针对移动机器人路径规划问题,提出一种基于QPSO算法的路径规划方法,并用概率论的方法分析了移动机器人路径规划的收敛性,阐明了该方法随均匀分布和正态分布的参数关系和收敛区间;然后根据移动机器人的运动特征提出一种改进的轨迹规划方法。移动机器人平台的实验结果表明了该方法在移动机器人路径规划中的有效性和可行性。     

电能表检验台体串联机械臂时间最优轨迹规划

时间最优轨迹规划有助于缩短机械臂运动时间,提高工作效率,在机械臂实际应用场景中起着至关重要的作用.针对串联机械臂点到点运动的时间最优轨迹规划问题,提出一种基于改进多种群遗传算法的最优轨迹规划方法.通过五次多项式插值对机械臂运动路径进行拟合,利用改进的多种群遗传算法对机械臂运动时间进行优化,改进之处包括:设计含有惩罚项的...     

An online collision-free trajectory generation algorithm for human–robot collaboration

The premise of human–robot collaboration is that robots have adaptive trajectory planning strategies in hybrid work cell. The aim of this paper is to propose a new online collision avoidance trajectory planning algorithm for moderate dynamic environments to insure human safety when sharing collaborative tasks. The algorithm contains two parts: trajectory generation and local optimization. Firstly, based on empirical Dirichlet Process Gaussian Mixture Model (DPGMM) distribution learning, a neural network trajectory planner called Collaborative Waypoint Planning network (CWP-net) is proposed to generate all key waypoints required for dynamic obstacle avoidance in joint space according to environmental inputs. These points are used to generate quintic spline smooth motion trajectories with velocity and acceleration constraints. Secondly, we present an improved Stochastic Trajectory Optimization for Motion Planning (STOMP) algorithm which locally optimizes the generated trajectories of CWP-net by constraining the trajectory optimization range and direction through the DPGMM model. Simulations and real experiments from an industrial use case of human–robot collaboration in the field of aircraft assembly testing show that the proposed algorithm can smoothly adjust the nominal path online and effectively avoid collisions during the collaboration.     

A novel cartesian trajectory planning method by using triple NURBS curves for industrial robots

This article presents a novel method of robot pose trajectory synchronization planning. First of all, based on triple NURBS curves, a method of describing the position and orientation synchronization of the robot is proposed. Then, through considering geometric and kinematic constraints, especially angular velocity constraint, and employing bidirectional interpolation algorithm, a robot pose trajectory planning approach is developed, which has limited linear jerk, continuous bounded angular velocity and approximate optimal time, and does not need an optimization program. Ultimately, two robot pose paths, blade-shaped curve and fan-shaped curve, are utilized for simulations, and the results indicate that the proposed trajectory planning method can satisfy the given constraint conditions, i.e. the linear jerk is limited and the angular velocity is continuous bounded. The trajectory tracking experiments are further carried out on a 6-DOF industrial robot, and the results show that the proposed planning method can generate smooth trajectories to ensure the stability of the robot motion without impact in practical situations.     

多运动体分布式最优编队构型形成算法

针对分布式通信条件下的多运动体编队构型形成问题进行研究.考虑到个体的有限通信与感知能力,传统集中式求解算法无法适应实际需求,提出一种基于分布式交替映射凸优化的分布式时间最优编队构型形成算法,使得个体间仅依赖局部通信与局部计算实现编队构型的快速形成;将该问题建模为含有等式约束的分布式Minimax凸优化问题,提出基于虚拟等式约束函数的分布式交替映射凸优化算法实现求解;根据求解结果,各运动体采用RVO避障策略实现最优构型形成.针对含有100个运动体的最优编队构型形成问题进行仿真,验证了所提出算法的有效性.     

Smooth trajectory planning for a parallel manipulator with joint friction and jerk constraints

In order to achieve better tracking accuracy effectively, a new smooth and near time-optimal trajectory planning approach is proposed for a parallel manipulator subject to kinematic and dynamic constraints. The complete dynamic model is constructed with consideration of all joint frictions. The presented planning problem can be solved efficiently by formulating a new limitation curve for dynamic constraints and a reduced form for jerk constraints. The motion trajectory is planned with quartic and quintic polynomial splines in Cartesian space and septuple polynomial splines in joint space. Experimental results show that smaller tracking error can be obtained. The developed method can be applied to any robots with analytical inverse kinematic and dynamic solutions.     

动态环境中基于改进粒子群的机器人路径规划

提出一种模糊隶属度函数对动态环境中机器人的运动状况进行建模,该建模方法不会无谓地牺牲机器人的可运动空间,可尽量减少机器人路径规划的约束强度;同时提出通过调整位置加权趋向无约束最优解的算子改进粒子群算法,提高算法的寻优速度。仿真结果表明,通过两者结合,可快速获得动态环境中的优化路径。     

基于粒子群优化的时间最优机械臂轨迹规划算法

根据机械臂运动学约束,提出了关节空间基于粒子群优化(PSO)的时间最优3-5-3多项式插值轨迹规划算法,解决了由于多项式插值轨迹规划具有阶次高、没有凸包性质等缺点,难以应用传统优化方法进行优化的问题.粒子群算法结构简单、参数易调整的特点弥补了多项式阶插值的缺点.直接在优化目标空间搜索,巧妙地避免了粒子群计算构造自变量和因变量的映射,降低了搜索维数,简化了计算.在优化过程中,采用两个适应度函数之间切换的开关控制,使各段插值尽快收敛于运动学约束内.通过与传统3-5-3多项式插值的运动位置、速度、加速度曲线对比,证明该方法运行时间更短,稳定性和流畅性更好.     

A Technique to Analytically Formulate and to Solve the 2-Dimensional Constrained Trajectory Planning Problem for a Mobile Robot

A new technique for trajectory planning of a mobile robot in a two-dimensional space is presented in this paper. The main concept is to use a special representation of the robot trajectory, namely a parametric curve consisting in a sum of harmonics (sine and cosine functions), and to apply an optimization method to solve the trajectory planning problem for the parameters (i.e., the coefficients) appearing in the sum of harmonics. This type of curve has very nice features with respect to smoothness and continuity of derivatives, of whatever order. Moreover, its analytical expression is available in closed form and is very suitable for both symbolic and numerical computation. This enables one to easily take into account kinematic and dynamic constraints set on the robot motion. Namely, non-holonomic constraints on the robot kinematics as well as requirements on the trajectory curvature can be expressed in closed form, and act as input data for the trajectory planning algorithm. Moreover, obstacle avoidance can be performed by expressing the obstacle boundaries by means of parametric curves as well. Once the expressions of the trajectory and of the constraints have been set, the trajectory planning problem can be formulated as a standard mathematical problem of constrained optimization, which can be solved by any adequate numerical method. The results of several simulations are also reported in the paper to show the effectiveness of the proposed technique to generate trajectories which meet all requirements relative to kinematic and dynamic constraints, as well as to obstacle avoidance.     

Simultaneous dynamic optimization: A trajectory planning method for nonholonomic car-like robots

Trajectory planning in robotics refers to the process of finding a motion law that enables a robot to reach its terminal configuration, with some predefined requirements considered at the same time. This study focuses on planning the time-optimal trajectories for car-like robots. We formulate a dynamic optimization problem, where the kinematic principles are accurately described through differential equations and the constraints are strictly expressed using algebraic inequalities. The formulated dynamic optimization problem is then solved by an interior-point-method-based simultaneous approach. Compared with the prevailing methods in the field of trajectory planning, our proposed method can handle various user-specified requirements and different optimization objectives in a unified manner. Simulation results indicate that our proposal efficiently deals with different kinds of physical constraints, terminal conditions and collision-avoidance requirements that are imposed on the trajectory planning mission. Moreover, we utilize a Hamiltonian-based optimality index to evaluate how close an obtained solution is to being optimal.     

Early prediction for physical human robot collaboration in the operating room

To enable a natural and fluent human robot collaboration flow, it is critical for a robot to comprehend their human peers’ on-going actions, predict their behaviors in the near future, and plan its actions correspondingly. Specifically, the capability of making early predictions is important, so that the robot can foresee the precise timing of a turn-taking event and start motion planning and execution early enough to smooth the turn-taking transition. Such proactive behavior would reduce human’s waiting time, increase efficiency and enhance naturalness in collaborative task. To that end, this paper presents the design and implementation of an early turn-taking prediction algorithm, catered for physical human robot collaboration scenarios. Specifically, a robotic scrub nurse system which can comprehend surgeon’s multimodal communication cues and perform turn-taking prediction is presented. The developed algorithm was tested on a collected data set of simulated surgical procedures in a surgeon–nurse tandem. The proposed turn-taking prediction algorithm is found to be significantly superior to its algorithmic counterparts, and is more accurate than human baseline when little partial input is given (less than 30% of full action). After observing more information, the algorithm can achieve comparable performances as humans with a F1 score of 0.90.     

运行时间周期化工业机器人模型迭代寻优NURBS轨迹插补

为满足工业机器人高精度复杂曲线运动的需求,本文提出运行时间周期化工业机器人模型迭代寻优NURBS轨迹插补算法.首先,根据轨迹最大轮廓误差和机器人动力学特性对曲线分段.随后,提出优化回溯算法,使各子曲线段均可用S曲线加减速规划.之后,为保证机器人在进给速度极小值处不超速,将各加减速阶段运行时间调整为插补周期的整数倍,并对子曲线段衔接处速度平滑处理.最后,提出模型迭代寻优曲线插补,大大降低了速度波动率.仿真试验表明,该方法插补轨迹的各项指标均满足要求且最大速度波动率仅为0.000099%.真机试验也验证了该方法可有效减小轨迹误差.     

Time-optimal tool motion planning with tool-tip kinematic constraints for robotic machining of sculptured surfaces

A time-optimal motion planning method for robotic machining of sculptured surfaces is reported in this paper. Compared with the general time-optimal robot motion planning, a surface machining process provides extra constraints such as tool-tip kinematic limits and complexity of the curved tool path that also need to be taken into account. In the proposed method, joint space and tool-tip kinematic constraints are considered. As there are high requirements for tool path following accuracy, an efficient numerical integration method based on the Pontryagin maximum principle is adopted as the solver for the time-optimal tool motion planning problem in robotic machining. Nonetheless, coupled and multi-dimensional constraints make it difficult to solve the problem by numerical integration directly. Therefore, a new method is provided to simplify the constraints in this work. The algorithm is implemented on the ROS (robot operating system) platform. The geometry tool path is generated by the CAM software firstly. And then the whole machine moving process, i.e. the feedrate of machining process, is scheduled by the proposed method. As a case study, a sculptured surface is machined by the developed method with a 6-DOF robot driven by the ROS controller. The experimental results validate the developed algorithm and reveal its advantages over other conventional motion planning algorithms for robotic machining.