Real-time navigation using randomized kinodynamic planning with arrival time field

In this paper, we propose a novel path planning algorithm for a mobile robot in dynamic and cluttered environments with kinodynamic constraints. We compute the arrival time field as a bias which gives larger weights for shorter and safer paths toward a goal. We then implement a randomized path search guided by the arrival time field for building the path considering kinematic and dynamic (kinodynamic) constraints of an actual robot. We also consider path quality by adding heuristic constraints on the randomized path search, such as reducing unstable movements of the robot by using a heading criterion. The path will be extracted by backtracking the nodes which reach the goal area to the root of the tree generated by the randomized search, and the motion from the very first node will be sent to the robot controller. We provide a brief comparison between our algorithm and other existing algorithms. Simulation and experimental results prove that our algorithm is fast and reliable to be implemented on the real robot and is able to handle kinodynamic problems effectively.     

Motion planning for tree climbing with inchworm‐like robots

This paper proposes a global path‐ and motion‐planning algorithm that enables inchworm‐like robots to navigate their way up tree branches. The intuitive climbing space representation method proposed here greatly simplifies the path‐planning problem. The dynamic programming algorithm can be used to identify the optimal path leading to the target position in the target direction according to the constraints and requirements specified. The planned path can be applied in any tree‐climbing robot that utilizes the nonenclosure gripping method. An efficient motion‐planning algorithm for continuum inchworm‐like robots is then developed to enable them to climb along the planned path with a high degree of accuracy. In comparison with the method proposed in our previous study, the method proposed herein significantly improves consistency between the planned path and the motions of the robot, and therefore makes it more practical to implement the motion‐planning algorithm in trees of different shapes. The paper also describes hardware experiments in which the proposed planning algorithm is applied to enable inchworm‐like robots to climb real trees, thus validating the proposed planning algorithm in practice. © 2012 Wiley Periodicals, Inc.     

基于区域行进策略的飞机油箱检查机器人路径规划算法

针对类似于飞机油箱环境中连续型机器人的路径规划问题,设计基于区域行进策略的路径规划算法,结合机器人本体结构约束规划到达油箱内任意给定目标点的路径。连续型机器人具有运动灵活性,但超冗余自由度导致了三维空间规划的多解性,增加了算法的复杂度。采用降低维度的方式,通过将三维空间转化为二维平面进行规划,降低了算法的时间复杂度。将飞机油箱的单舱划分为两个区域,根据目标点所处区域位置确定规划策略。最后,基于Matlab对所提算法进行仿真,实验结果验证了算法的可行性和有效性。     

基于混合策略的轮式机器入路径规划方法

快速扩展随机树方法（R RT）是解决具有非完整性约束的轮式机器人路径规划问题的一种有效途径。R RT能够在规划过程中引入机器人动力学约束,但是当环境中存在大量障碍物时,R RT算法的路径搜索效率将会降低。另一方面,R RT算法不具有最优性,限制了其在轮式机器人路径规划中的应用。针对经典R RT算法的不足,提出一种混合的路径规划策略,首先通过路径导引点扩展多树R RT结构,利用多树R RT的局部探索与合并特性快速寻找可通行的区域范围,利用启发式搜索算法在可通行区域内快速寻找动力学可行的机器人运动轨迹。仿真与实车实验表明,该方法能够快速有效地解决复杂障碍物环境下的机器人路径规划问题。     

约束条件下的巡线机器人逆运动学求解

高压输电线路巡检机器人是一种多关节悬挂运动机构,要实现其运动控制就需要根据机器人的本身机构特点和悬挂系统的运动约束条件进行运动学分析.本文利用微分扭转法对巡线机器人的正向运动学进行了求解,并通过对机器人悬挂系统的力学分析,得到了机器人运动学的约束条件,并在这种约束条件下,采用了一种可用来进行实时控制的迭代循环坐标下降(CCD)算法,来进行机器人的逆运动学求解.这种迭代算法对于有运动约束系统的逆运动学求解具有较强的适用性,而且它具有较好的收敛性和有效性,适合于在线计算,便于巡线机器人的实时运动控制.     

A path conditioning method with trap avoidance

This work presents a sliding-mode method for robotic path conditioning. The proposal includes a trap avoidance algorithm in order to escape from trap situations, which are analogous to local minima in potential field-based approaches. The sliding-mode algorithm activates when the desired path is about to violate the robot workspace constraints, modifying it as much as necessary in order to fulfill all the constraints and reaching their limit surface at low speed. The proposed path conditioning algorithm can be used on-line, since it does not require a priori knowledge of the desired path, and improves the conventional conservative potential field-based approach in the sense that it fully exploits the robot workspace. The proposed approach can be easily added as an auxiliary supervisory loop to conventional robotic planning algorithms and its implementation is very easy in a few program lines of a microprocessor. The proposed path conditioning is compared through simulation with the conventional potential field-based approach in order to show the benefits of the method. Moreover, the effectiveness of the proposed trap avoidance algorithm is evaluated by simulation for various trap situations.     

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.     

仿蛇变体机器人运动机理研究

本文设计了一种蛇形机器人，分析了蛇形机器人的结构，详细讨论了蛇形机器人的运 动机理和几何结构关系，并推导出蛇形机器人的控制算法和相应的控制程序，蛇形机器人在 程序控制下能够向前、向后运动，在一定程度上实现了蛇的运动．     

Spline-Based RRT Path Planner for Non-Holonomic Robots

Planning in a cluttered environment under differential constraints is a difficult problem because the planner must satisfy the external constraints that arise from obstacles in the environment and the internal constraints due to the kinematic/dynamic limitations of the robot. This paper proposes a novel Spline-based Rapidly-exploring Random Tree (SRRT) algorithm which treats both the external and internal constraints simultaneously and efficiently. The computationally expensive numerical integration of the system dynamics is replaced by an efficient spline curve parameterization. In addition, the SRRT guarantees continuity of curvature along the path satisfying any upper-bounded curvature constraints. This paper presents the underlying theory to the SRRT algorithm and presents simulation and experiment results of a mobile robot efficiently navigating through cluttered environments.     

机器人的CP-nets优化类人轨迹规划

机器人移动轨迹按照人的手臂来模拟是提高机器人安全性和人机交互能力的有效方法,特别是针对机器人抓取路径不唯一的场合,类人行为对于人机系统表现更加自然。此前,通常利用Kinect等设备,基于人工神经网络和K近邻算法等智能算法对类人轨迹进行规划,但无法获得未采样过的最优轨迹。本文基于CP-nets采用偏好模型研究类人运动轨迹,然后将该模型应用于机器人控制,在没有采样的情况下,也可得到最优的类人轨迹。实验结果表明,基于CP-nets 的类人规划轨迹具有较高的效率和舒适性,符合人的运动特征。     

A case study of two-robot-arm workcell material handling

More problems are involved in collaborating multi-robot-arm systems than in single robot arms. These problems stem from mutual dynamic and kinematic effects between the arms. This work is confined to only the kinematics of two robot arms; other problems like control, force distribution, and so on are not addressed here. A particular case of a material handling problem with two collaborating robot arms loading/unloading long objects from a conveyor is studied. The feasibility of the task from a kinematics point of view, and the necessary conditions and constraints for the relative set-up of the two manipulators are discussed. These conditions originate from the working envelope of the two arms which depends on three factors: the working envelope of each individual arm, the spacing between them, and the dimensions of the workpiece. To assist this study, an efficient algorithm for determining the two-dimensional contours of the workspace of a single arm is included.     

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

Integrated motion planning and control for the purposes of maneuvering mobile robots under state- and input constraints is a problem of vital practical importance in applications of mobile robots such as autonomous transportation. Those constraints arise naturally in practice due to specifics of robot mechanical construction and the presence of obstacles in motion environment. In contrast to approaches focusing on feedback control design under the assumption of given reference motion or motion planning with neglection of subsequent feedback motion execution, we adopt a controller-driven motion planning paradigm, which has recently gained attention of many researchers. It postulates design of motion planning algorithms dedicated to specific feedback control policies, which compute a sequence of feedback control subtasks instead of classically planned open-loop controls or parametric paths. In this spirit, we propose a motion planning algorithm driven by the VFO (Vector Field Orientation) control law for the waypoint-following task. Presented analysis of the VFO control law reveals its beneficial properties, which are subsequently utilized to solve a generally nonlinear and non-convex optimal motion planning problem by formulating it as a mixed-integer linear program (MILP). The solution proposed in this paper yields a waypoint sequence, which is designed for execution by application of the VFO control law to drive a robot to a prescribed final configuration under an input constraint imposed by bounded curvature of robot motion and state constraints resulting from a convex decomposition of task space. Satisfaction of these constraints is guaranteed analytically and exactly, i.e., without utilization of numerical approximations. Moreover, for a given discrete set of possible waypoint orientations, the proposed algorithm computes plans optimal w.r.t. given cost functional, which can be any convex linear combination of quantities such as robot path length, curvature of robot motion, distance to imposed state constraints, etc. Furthermore, the planning algorithm exploits the possibility of both forward or backward movement of the robot to allow maneuvering in demanding environments. Generated waypoint sequences are a compact representation of a motion plan, which can be immediately executed with the VFO controller without any additional post-processing. Validity of the proposed approach has been confirmed by simulation studies and experimental motion execution with a laboratory-scale mobile robot.     

基于多约束条件的机器人抓取策略学习方法

针对机器人在多类别物体不同任务下的抓取决策问题,提出基于多约束条件的抓取策略学习方法.该方法以抓取对象特征和抓取任务属性为机器人抓取策略约束,通过映射人类抓取习惯规划抓取模式,并采用物体方向包围盒(OBB)建立机器人抓取规则,建立多约束条件的抓取模型.利用深度径向基(DRBF)网络模型结合减聚类算法(SCM)实现抓取策略的学习,两种算法的结合旨在提高学习鲁棒性与精确性.搭建以Refiex 1型灵巧手和AUBO六自由度机械臂组成的实验平台,对多类别物体进行抓取实验.实验结果表明,所提出方法使机器人有效学习到对多物体不同任务的最优抓取策略,具有良好的抓取决策能力.     

Grasp capability analysis of multifingered robot hands

This paper addresses the problem of grasp capability analysis of multifingered robot hands. The aim of the grasp capability analysis is to find the maximum external wrench that the multifingered robot hands can withstand, which is an important criterion in the evaluation of robotic systems. The study of grasp capability provides a basis for the task planning of force control of multifingered robot hands. For a given multifingered hand geometry, the grasp capability depends on the joint driving torque limits, grasp configuration, contact model and so on. A systematic method of the grasp capability analysis, which is in fact a constrained optimization algorithm, is presented. In this optimization, the optimality criterion is the maximum external wrench, and the constraints include the equality constraints and the inequality constraints. The equality constraints are for the grasp to balance the given external wrench, and the inequality constraints are to prevent the slippage of fingertips, the overload of joint actuators, the excessive forces over the physical limits of the object, etc. The advantages of this method are the ability to accomodate diverse areas such as multiple robot arms, intelligent fixtures and so on. The effectiveness of the proposed method is confirmed with a numerical example of a trifingered grasp.     

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.

     

基于改进混沌搜索算法的机器人轨迹规划

在考虑机器人关节约束的影响下,为得到工业机器人的时间最优轨迹,提出了一种适用于多极值函数优化问题的混合算法。首先基于混沌搜索算法定位最优解的邻域,继而使用遗传算法在此邻域内寻找最优解。在MATLAB平台上,对该混合算法进行编程并仿真轨迹,并与传统遗传算法的结果进行比较,结果表明使用混合算法得到的总时间为25.449 s,明显少于对照组的39.534 s,证实了该混合算法具有较好的全局搜索性能。     

通讯受限条件下的机器人编队算法

针对通讯受限条件下大规模移动机器人编队任务, 本文提出了基于行为的分布式多机器人线形编队控制 和避障算法. 机器人个体无需获得群体中所有机器人的信息, 而是根据传感器获取的环境信息和局部范围内的机器 人信息对其自身的调整方向进行预测, 并最终很好地完成了设定的编队及避障任务. 由于本文方法需求的通讯量不 大, 并且采用分布式控制, 因此该方法适用于大规模的机器人集群编队任务. 文中还给出了本系统的稳定性分析, 证 明了系统的稳定性. 实验结果表明该算法使得机器人能够仅通过局部信息形成线形编队, 在遇到障碍物后能够灵活 避开障碍物, 并且在避开障碍物进入安全区域后重新恢复线形编队.     

A New Optimal Force Distribution Scheme of Multiple Cooperating Robots Using Dual Method

A new optimal force distribution scheme of multiple cooperating robots is proposed, in which the duality theory of nonlinear programming (NLP) is combined with the quadratic programming (QP) approach. The optimal force distribution problem is formulated as a QP problem with both linear and quadratic constraints, and its solution is obtained by an efficient algorithm. The use of the quadratic constraints is important in that it considerably reduces the number of constraints, thus enabling the Dual method of NLP to be used in the solution algorithm. Moreover, it can treat norm constraints without approximation, such as bound of the norm of the force exerted by each robot. The proposed scheme is more efficient in terms of speed than any other method. Numerical examples of two PUMA robot task using the proposed method and a well-known fast method are compared, and the results indicate the capability of real time application of our method.     

A novel obstacle avoidance algorithm: “Follow the Gap Method”

In this paper, a novel obstacle avoidance method is designed and applied to an experimental autonomous ground vehicle system. The proposed method brings a new solution to the problem and has several advantages compared to previous methods. This novel algorithm is easy to tune and it takes into consideration the field of view and the nonholonomic constraints of the robot. Moreover the method does not have a local minimum problem and results in safer trajectories because of its inherent properties in the definition of the algorithm. The proposed algorithm is tested in simulations and after the observation of successful results, experimental tests are performed using static and dynamic obstacle scenarios. The experimental test platform is an autonomous ground vehicle with Ackermann steering geometry which brings nonholonomic constraints to the vehicle. Experimental results show that the task of obstacle avoidance can be achieved using the algorithm on the autonomous vehicle platform. The algorithm is very promising for application in mobile and industrial robotics where obstacle avoidance is a feature of the robotic system.     

基于蚁群和带空间约束FCM的荔枝图像分割算法

准确地提取荔枝果实的完整轮廓对采摘机器人自动识别与采摘至关重要。以蚁群和模糊C均值（FCM）聚类为理论基础,选用符合荔枝颜色特性的L*a*b*颜色空间,提出一种基于蚁群和带空间约束FCM的荔枝图像分割算法。该算法利用L*a*b*颜色空间的a*通道正轴代表红色和负轴代表绿颜色进行初始分割,然后利用蚁群聚类算法全局性和鲁棒性的优点确定FCM的聚类中心,用引入空间约束的FCM完整地分割出荔枝果实。实验结果表明此方法实现了荔枝图像完整地分割,并且满足了采摘机器人后续的荔枝识别与采摘,对成熟荔枝分割的正确率达到了87%。