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。     

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。     

Time-optimal and Smooth Trajectory Planning for Robot Manipulators

This paper presents a practical time-optimal and smooth trajectory planning algorithm and then applies it to robot manipulators. The proposed algorithm uses the time-optimal theory based on the dynamics model to plan the robot’s motion trajectory, constructs the trajectory optimization model under the constraints of the geometric path and joint torque, and dynamically selects the optimal trajectory parameters during the solving process to prominently improve the robot’s motion speed. Moreover, the proposed algorithm utilizes the input shaping algorithm instead of the jerk constraint in the trajectory optimization model to achieve a smooth trajectory. The input shaping of trajectory parameters during postprocessing not only suppresses the residual vibration of the robot but also takes the signal delay caused by traditional input shaping into account. The combination of these algorithms makes the proposed time-optimal and smooth trajectory planning algorithm ensure absolute time optimality and achieve a smooth trajectory. The results of an experiment on a six-degree-of-freedom industrial robot indicate the validity of the proposed algorithm.

     

Singularity robust path planning for real time base attitude adjustment of free-floating space robot

This paper presents a singularity robust path planning for space manipulator to achieve base (satellite) attitude adjustment and end-effector task. The base attitude adjustment by the movement of manipulator will save propellant compared with conventional attitude control system. A task-priority reaction null-space control method is applied to achieve the primary task of adjusting attitude and secondary task of accomplishing end-effector task. Furthermore, the algorithm singularity is eliminated in the proposed algorithm compared with conventional reaction null-space algorithm. And the singular value filtering decomposition is introduced to dispose the dynamic singularity, the unit quaternion is also introduced to overcome representation singularity. Hence, a singularity robust path planning algorithm of space robot for base attitude adjustment is derived. A real time simulation system of the space robot under Linux/RTAI (realtime application interface) is developed to verify and test the feasibility and reliability of the method. The experimental results demonstrate the feasibility of online base attitude adjustment of space robot by the proposed algorithm.     

Study on Non-holonomic Cartesian Path Planning of a Free-Floating Space Robotic System

The non-holonomic characteristic of a free-floating space robotic system is used to plan the path of the manipulator joints, by whose motion the base attitude and the inertial pose (the position and orientation with respect to the inertial frame) of the end-effector attain the desired values. First, the kinematic equations of a free-floating space robot are simplified and the system state variables are transformed to another form composed of base attitude and joint angles. Then, the joint trajectories are parameterized using sinusoidal functions, whose arguments are seven-order polynomials. Third, the planning problem is transformed to an optimization problem; the cost function, defined according to the accuracy requirements of system variables, is the function of the parameters to be determined. Finally, the Particle Swarm Optimization (PSO) algorithm is used to search the solutions of the parameters that determine the joint trajectories. The presented method meets three typical applications: (i) point-to-point maneuver of the end-effector without changing the base attitude, (ii) attitude maneuver of the base without changing the end-effector's pose and (iii) point-to-point maneuver of the end-effector with adjusting the base attitude synchronously. The simulation results of a spacecraft with a 6-d.o.f. manipulator verify the performance and the validity of the proposed method.     

Fuzzy-based-admittance controller for safe natural human–robot interaction

ABSTRACT

In recent years, a great amount of research on physical human–robot interaction has been conducted, and mainly concentrated on safety issues to minimize the risk of accidents to the operator during the cooperation between human and robot. Unfortunately, the identification of inertia and damping matrices in the dynamic admittance model is time-consuming, which is still an open problem of previous admittance controllers. Additionally, the natural cooperation is that cooperative movements are implemented in every degree of freedom in space, which is rarely concerned while it is important to implement more complex cooperative movements, and to help operator feels naturally during the cooperation. This paper presents an alternative admittance controller based on inference mechanism of fuzzy logic to eliminate the identification of inertia and damping matrices during the process of controller formulation in which the end-effector’s velocity is adaptively adjusted via external wrench (force/torque measured by a sensor mounted on end-effector) and power transmitted by the robot. Moreover, the proposed controller also considers end-effector’s full DOF to guarantee the natural human–robot interaction. The fuzzy-admittance controller is evaluated by an experimental set-up of teaching task using 6-DOF manipulator in which manipulator moves passively via the human impact on real-time force/torque sensor mounted on end-effector.     

Collision-free motion planning of dual-arm reconfigurable robots

Dual-arm reconfigurable robot is a new type of robot. It can adapt to different tasks by changing its different end-effector modules which have standard connectors. Especially, in fast and flexible assembly, it is very important to research the collision-free planning of dual-arm reconfigurable robots. It is to find a continuous, collision-free path in an environment containing obstacles. A new approach to the real-time collision-free motion planning of dual-arm reconfigurable robots is used in the paper. This method is based on configuration space (C-Space). The method of configuration space and the concepts reachable manifold and contact manifold are successfully applied to the collision-free motion planning of dual-arm robot. The complexity of dual-arm robots’ collision-free planning will reduce to a search in a dispersed C-Space. With this algorithm, a real-time optimum path is found. And when the start point and the end point of the dual-arm robot are specified, the algorithm will successfully get the collision-free path real time. A verification of this algorithm is made in the dual-arm horizontal articulated robot SCARATES, and the simulation and experiment ascertain that the algorithm is feasible and effective.     

Path planning with general end-effector constraints

In this paper, we address the path planning problem with general end-effector constraints (PPGEC) for robot manipulators. Two approaches are proposed. The first approach is the Adapted-RGD method, which is adapted from an existing randomized gradient descent (RGD) method for closed-chain robots. The second approach is radically different. We call it ATACE, Alternate Task-space And Configuration-space Exploration. Unlike the first approach which searches purely in C-space, ATACE works in both task space and C-space. It explores the task space for end-effector paths satisfying given constraints, and utilizes trajectory tracking technique(s) as a local planner(s) to track these paths in the configuration space. We have implemented both approaches and compared their relative performances in different scenarios. ATACE outperforms Adapted-RGD in the majority (but not all) of the scenarios. We outline intuitive explanations for the relative performances of these two approaches.     

多地貌环境下的移动机器人路径规划研究

针对多地貌环境下的移动机器人路径规划问题,建立多目标优化模型,并采用微粒群算法解决该问题.首先,采用区域权值表示机器人在各种地形下的通行困难度;然后,结合局部优化准则计算机器人的通行时间,通过计算机器人与危险源之间覆盖的面积来衡量路径的危险程度,并将上述问题转化为两目标优化问题;最后,采用多目标微粒群优化算法优化上述问题.仿真结果表明了所提出方法的有效性.     

FOV Constraint Region Analysis and Path Planning for Mobile Robot with Observability to Multiple Feature Points

In visual servoing tasks, it is an important problem to maintain the observability to feature points on objects, which are usually used to calculate the pose between objects and robots. In particular, when the robot’s vision has a limited field of view (FOV) and the points on objects are distributed separately, the problem is more serious. In this paper, based on FOV constraint region analysis and path planning, we propose a novel method for a mobile robot equipped with a pan-tilt camera to keep all points on objects in its view. According to the Horizontal-FOV and Vertical-FOV angular aperture of camera, bounding boxes assisting to calculate the regions with FOV constraint are acquired firstly. Then the region where the robot inside it cannot keep all points in its view can be obtained. Finally the mobile robot plans a shortest path from the current position to the destination, which can avoid the region with FOV constraint. The results of simulations and experiments prove that our method can make mobile robot keep all feature points in its view when it is moving.

     

基于改进蝙蝠算法和三次样条插值的机器人路径规划

为更好地解决移动机器人路径规划问题, 改进蝙蝠算法的寻优性能, 拓展其应用领域, 提出了一种具有反向学习和正切随机探索机制的蝙蝠算法. 在全局搜索阶段的位置更新中引入动态扰动系数, 提高算法全局搜索能力; 在局部搜索阶段, 融入正切随机探索机制, 增强算法局部寻优的策略性, 避免算法陷入局部极值. 同时, 加入反向学习选择策略, 进一步平衡蝙蝠种群多样性和算法局部开采能力, 提高算法的收敛精度. 然后, 把改进算法与三次样条插值方法相结合去求解机器人全局路径规划问题, 定义了基于路径结点的编码方式, 构造了绕避障碍求解最短路径的方法和适应度函数. 最后, 在简单和复杂障碍环境下分别对单机器人和多机器人系统进行了路径规划对比实验. 实验结果表明, 改进后算法无论在最优解还是平均解方面都要优于其他几种对比算法, 对于求解机器人全局路径规划问题具有较好的可行性和有效性.     

Robotic surgery setup simulation with the integration of inverse-kinematics computation and medical imaging

At present, there are representative robot operation systems such as da Vinci and ZEUS which have realized minimally invasive surgery by the use of dexterous manipulators. In the operating room, medical staff must prepare and set up an environment in which the robot has optimal freedom of motion and its functions can be fully demonstrated for every case. The range of motion in which the robot can reach and be maneuvered is restricted by the fixed point of the trocar site. We have developed a preoperative planning system with the function of volume rendering of medical images and automatic positioning by applying an inverse-kinematics computation of surgical robot. The motion of a surgical robot can be simulated in advance with the intuitive interface and kinematics computation program running in the background of the system. If robotic surgery planning with volume rendering of DICOM images is possible, the discussion of a surgical plan can be directly made just after the diagnosis considering the patient-specific structure. This kind of setup platform would be essential for the future introduction of surgical robotics into an operating room.     

Path following by the end-effector of a redundant manipulator operating in a dynamic environment

This paper addresses the problem of generating at the control-loop level a collision-free trajectory for a redundant manipulator operating in dynamic environments which include moving obstacles. The task of the robot is to follow, by the end-effector, a prescribed geometric path given in the work space. The control constraints resulting from the physical abilities of robot actuators are also taken into account during the robot movement. Provided that a solution to the aforementioned robot task exists, the Lyapunov stability theory is used to derive the control scheme. The numerical simulation results for a planar manipulator whose end-effector follows a prescribed geometric path, given in both an obstacle-free work space and a work space including the moving obstacles, illustrate the trajectory performance of the proposed control scheme.     

Optimal regulation of a cable suspended robot equipped with cable interfering avoidance controller

Closed-loop regulation of a spatial cable suspended robot is performed in this paper subject to maximizing the Dynamic Load Carrying Capacity (DLCC) of the end-effector while the cable interference is avoided actively. Optimization is performed between two predefined boundaries and considering the cable interference constraint. This constraint is satisfied by designing a controller which prevents the cables’ collision. The overall formulation of the closed-loop optimal control based on Feedback linearization is derived in this paper for planning the optimal path with the highest load capacity. Then a complementary adaptive controller is designed and implemented to the main controller which is responsible for providing cable interfering avoidance. The efficiency of the designed controller for preventing the cables’ collision is shown by performing and analyzing some comparative simulations conducted on an under constrained cable robot with six cables and six DOFs. All results related to regulation, tracking and DLCC are compared between the simple optimal closed-loop system and the system which is equipped with the proposed cable interfering avoidance controller. It is proved that the planned path satisfies cable interference constraint while its DLCCs are optimized.     

Path Planning of Free Floating Prismatic-Jointed Manipulators

Reaching a desired position with a specific orientationin space by a robot, mounted on a freely floating base, is an importantpath planning and control problem. Research in this area has mainlyconcentrated on the use of revolute-jointed serial manipulators. It iswell known that the dynamic equations of such manipulators are quitecomplex.In this paper, we propose the use of a 6-link fully prismatic-jointedrobot to achieve a desired position and orientation in space instead of arevolute-jointed robot. The use of pure prismatic-jointed robots forsuch a purpose is counter intuitive. On earth, such a structure is unableto provide a desired orientation to the end-effector. However, it can beshown that in space, arbitrary end-effector orientations are possible.Due to the relative simplicity of kinematics, dynamics and workspace ofprismatic-jointed robots compared to revolute-jointed robots, their useresults in significant computational advantages in path planning andcontrol.Also, in this paper, we adopt an unconventional motion planning methodthat avoids inversion of the Jacobian matrix and results in singularityfree paths for the end-effector. In this method, the joint trajectoriesare considered to be modal sums of basis functions of time. Within this framework, constraints on jointangles and joint rates can be imposed. The results are demonstratedwith an example of a 6-link fully prismatic-jointed robot.     

部分未知环境中移动机器人动态路径规划方法

针对部分未知环境,提出一种基于粒子滤波的动态路径规划方法.将全局最优路径视为受机器人运动及环境影响的变化量,采用粒子滤波算法,利用机器人运动信息预测路径,并利用实时环境信息更新路径,通过在线跟踪全局最优路径获得不断更新的全局优化路径.将传统全局路径规划先规划后执行的模式改为边规划边执行的模式,既减少了等待时间,又为机器人的移动误差及部分未知环境提供了较强的适应能力.仿真及实验验证,该方法的有效性.     

A novel AR-based robot programming and path planning methodology

This paper discusses the benefits of applying Augmented Reality (AR) to facilitate intuitive robot programming, and presents a novel methodology for planning collision-free paths for an n degree-of-freedom (DOF) manipulator in an unknown environment. The targeted applications are where the end-effector is constrained to move along a visible 3D path/curve, which position is unknown, at a particular orientation with respect to the path, such as arc welding and laser cutting. The methodology is interactive as the human is involved in obtaining the 3D data points of the desired curve to be followed through performing a number of demonstrations, defining the free space relevant to the task, and planning the orientations of the end-effector along the curve. A Piecewise Linear Parameterization (PLP) algorithm is used to parameterize the data points using an interactively generated piecewise linear approximation of the desired curve. A curve learning method based on Bayesian neural networks and reparameterization is used to learn and generate 3D parametric curves from the parameterized data points. Finally, the orientation of the end-effector along the learnt curve is planned with the aid of AR. Two case studies are presented and discussed.     

动态未知环境中移动机器人的滚动路径规划及安全性分析

借鉴预测控制滚动优化原理,研究了全局环境未知且存在动态障碍物情况下的机器人路径规划问题.提出的基于滚动窗口的移动机器人路径规划方法充分利用机器人实时测得的局部环境信息,以滚动方式进行在线规划,合理结合了优化与反馈,对动态环境具有良好的适应性.还对规划算法的安全性进行了分析.     

改进RRT-人工势场法的机械臂堆垛运动方法

快速拓展随机树算法(RRT)在机械臂路径规划中存在随机性强、搜索效率低、规划路径长等问题,不能在货柜堆垛场景中取得相对最优的光滑路径.对此,该文提出了一种改进RRT-人工势场法混合算法进行货柜堆垛机械臂运动规划.首先,对传统快速拓展随机树算法进行改进,在传统快速拓展随机树算法的全局搜索的基础上引入目标搜索,增强了随机树...     

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.