Multi-objective dynamic optimization with genetic algorithms for automatic parking

This paper addresses the problem of automatic parking by a back-wheel drive vehicle, using a biomimetic model based on direct coupling between vehicle perceptions and actions. This problem is solved by means of a bio-inspired approach in which the vehicle controller does not need to know the car kinematics and dynamic, neither does it call for a priori knowledge of the environment map. The key point in the proposed approach is the definition of performance indices that for automatic parking happen to be functions of the strategic orientations to be injected, in real time, to the car-like robot controller. This solution leads to a dynamic multi-objective optimization problem, which is extremely hard to be dealt analytically. A genetic algorithm is therefore applied, thanks to which we obtain a very simple and efficient solution.     

Nonholonomic distance to polygonal obstacles for a car-like robot of polygonal shape

This paper shows how to compute the nonholonomic distance between a polygonal car-like robot and polygonal obstacles. The solution extends previous work of Reeds and Shepp by finding the shortest path to a manifold (rather than to a point) in configuration space. Based on optimal control theory, the proposed approach yields an analytic solution to the problem.     

A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization

In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.     

一种车型机器人路径规划方法

在自主移动机器人的许多应用中,路径规划技术顺序地设置一套分散的路径点来引导机器人以最短的时间从起始位置到达目标点。针对移动机器人路径规划问题,提出了一种非完整型机器人路径规划技术,该技术采用基本原子操纵方法来解决车型机器人路径规划问题,并采用平滑路径规划方法来产生更多的连续路径用以解决基本原子操纵技术在做路径规划时具有很不连续的缺点从而为机器人获得最优路径。仿真结果证明了该方法的有效性和实用性。     

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.     

车型移动机器人的SPRM路径规划方法

研究车型移动机器人的路径规划问题,提出一种用局部规则路图结合随机路图,辅助建立全局复合路图的环境建模方法. 在此基础上进行路径规划,提高了在障碍物附近产生的局部路径的质量,减少了由于频繁地执行避碰校验所造成的时间消耗,并解决了可行空间丢失的问题. 仿真实验验证了这种方法在车形移动机器人路径规划应用中的有效性.     

A linearizing feedback for stabilizing a car-like robot following a curvilinear path

The path following problem for a car-like robot is considered. The control goal is to bring the robot to a pre-assigned curvilinear path and to stabilize its motion along the path. A new canonical change of variables is suggested. It reduces the problem of stabilizing robot’s motion to that of stability of the zero solution of the transformed system in the form that admits feedback linearization. A new control law is synthesized that ensures linearity of the closed-loop system and stabilizes robot’s motion along a given target path if the initial conditions belong to a known region. Comparison of the new control law with two earlier obtained linearizing feedbacks known from the literature demonstrates its unquestionable advantages.     

一种基于概率方法的车型机器人路径规划方法

考虑车体外形和前轮方向角限制,车型移动机器人路径的空间维数明显增加,常规的规划方法难以获得理想的速度。论文报告了概率方法在车型机器人路径规划上的研究结果,共研究了机器人约束、全局、局部概率路径规划和启发式结点生成等问题。模拟表明,该方法可获得足够好的适合车型机器人移动的次优路径。     

Sensor-Based Control Architecture for a Car-Like Vehicle

This paper presents a control architecture endowing a car-like vehicle moving in a dynamic and partially known environment with autonomous motion capabilities. Like most recent control architectures for autonomous robot systems, it combines three functional components: a set of basic real-time skills, a reactive execution mechanism and a decision module. The main novelty of the architecture proposed lies in the introduction of a fourth component akin to a meta-level of skills: the sensor-based manoeuvers, i.e., general templates that encode high-level expert human knowledge and heuristics about how a specific motion task is to be performed. The concept of sensor-based manoeuvers permit to reduce the planning effort required to address a given motion task, thus improving the overall response-time of the system, while retaining the good properties of a skill-based architecture, i.e., robustness, flexibility and reactivity. The paper focuses on the trajectory planning function (which is an important part of the decision module) and two types of sensor-based manoeuvers, trajectory following and parallel parking, that have been implemented and successfully tested on a real automatic car-like vehicle placed in different situations.     

Learning optimal trajectories for non-holonomic systems

Many advanced robotic systems are subject to non-holonomic constraints, e.g. wheeled mobile robots, space manipulators and multifingered robot hands. Steering these mechanisms between configurations in the presence of perturbations is a difficult problem. In fact, the divide et impera strategy (first plan a trajectory, then track it by feedback) has a fundamental drawback in this case: due to the peculiar control properties of non-holonomic systems, smooth feedback cannot provide tracking of the whole trajectory. As a result, it would be necessary to give up either accuracy in the final positioning or predictability of the actual motion. We pursue here a different approach which does not rely on a separation between planning and control. Based on the learning control paradigm, a robust steering scheme is devised for systems which can be put in chained form, a canonical structure for non-holonomic systems. By overparametrizing the control law, other performance goals can be met, typically expressed as cost functions to be minimized along the trajectory. As a case study, we consider the generation of robust optimal trajectories for a car-like mobile robot, with criteria such as total length, maximum steering angle, distance from workspace obstacles, or error with respect to an offline planned trajectory.     

An automatic parking system using an optimized image-based fuzzy controller by genetic algorithms

An automatic parking system of a car-like mobile robot is an important issue in commercial applications. An image-based fuzzy controller for an automatic parking system of a car-like mobile robot was developed in previous work, where the membership functions were tuned by experimentally. The aim of this paper is to optimize the parameters of the membership functions, which were performed in previous work, using a genetic algorithm against the complicated tuning of the controller. The details of GA implementation, such as the design parameters and choice of fitness function, are described. Simulation results illustrate the effectiveness of the developed schemes.     

基于Adams和Matlab联合仿真的双足机器人越障研究

与轮行机器人相比,双足机器人具有更灵活的机械结构,具有跨越静态或动态障碍物的能力,使其可以在更复杂的环境中工作;以往的双足机器人路径规划控制策略只能解决静止或以可预测速度运动的障碍物的越障问题,提出了一种基于模糊Q学习算法的路径规划策略,在Adams软件中建立机器人的三维虚拟样机模型,在Matlab软件中设计控制器,进行联合仿真;仿真结果表明所设计的控制策略可以有效地克服机器人在线学习时间长的问题,并且可以成功跨越速度不可预测的运动障碍物,有很好的鲁棒性。     

Mobile Robot Command by Man–Machine Co-Operation – Application to Disabled and Elderly People Assistance

Disabled people assistance is developing thanks to progress of new technologies. A manipulator arm mounted on a mobile robot can assist a disabled person for a partial restoration of the manipulative function. A user pilots the robot via a control station, using enhanced reality techniques. To be affordable such a system must be cost-effective. That constraint limits perception means: ultrasonic ring, dead reckoning, and low-cost camera. The development of the project has followed two stages. The first one consists of giving maximum autonomy capacities to the robot for planning, navigation, and localisation. The second stage is the study of the Man–Machine Co-operation (MMC) for the command of the robot system. Indeed, the aim is to perform a mission (mobile robot displacement), using the robot capacities and man possibilities. Users build their own strategies to carry out a mission successively. The strategy can be seen as a succession of control modes, which can be manual, automatic, or shared. In the latter case, the control of the robot is shared between a human operator and machine. The main problem is then task allocation between both intelligent entities. Each one has planification, navigation, and localisation abilities. The paper presents our approach for planning and navigation and develops a more specific study about robot localisation.     

Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints

In this paper a high smooth trajectory planning method is presented to improve the practical performance of tracking control for robot manipulators. The strategy is designed as a combination of the planning with multi-degree splines in Cartesian space and multi-degree B-splines in joint space. Following implementation, under the premise of precisely passing the via-points required, the cubic spline is used in Cartesian space planning to make either the velocities or the accelerations at the initial and ending moments controllable for the end effector. While the septuple B-spline is applied in joint space planning to make the velocities, accelerations and jerks bounded and continuous, with the initial and ending values of them configurable. In the meantime, minimum-time optimization problem is also discussed. Experimental results show that, the proposed approach is an effective solution to trajectory planning, with ensuring a both smooth and efficiency tracking performance with fluent movement for the robot manipulators.     

An integrated robust probing motion planning and control scheme: A tube-based MPC approach

‘This paper introduces the integration of a probing scheme into a robust MPC-based robot motion planning and control algorithm. The proposed solution tackles the output-feedback tube-based MPC problem using the partially-closed loop strategy to incorporate future measurements in a computationally efficient manner. This combination will provide not only a robust controller but also avoids overly conservative planning which is a drawback of the original implementation of the output-feedback tube-based MPC. The proposed solution is composed of two controllers: (i) a nominal MPC controller with probing feature to plan a globally convergent trajectory in conjunction with active localization, and (ii) an ancillary MPC controller to stabilize the robot motion around the planned trajectory. The performance and real-time implementation of the proposed planning and control algorithms have been verified through both extensive numerical simulations and experiments with a mobile robot.     

Collision avoidance for Robot manipulators: Application to Catia/IBM 7565 interface

A collision avoidance algorithm has been developed to augment the capability of an automatic (off-line) robot path planning (programming) tool. The use of off-line programming tools for robot task programming is becoming increasingly important, but the advantages to be gained by off-line programming may be lost if collision-free path planning capabilities are not included. This article addressed the problem of collision-free path planning in the context of a gantry type robot. The collision avoidance algorithm described here uses the <heuristic approach> to collision-free path planning. The manipulator and obstacles are modeled as spheres to simplify tests for collision. An important feature of this algorithm is that it permits the manipulation of objects in the robot's environment. When compared against an algorithm from the literature, given a lightly cluttered environment modelled by spheres, the new algorithm finds a collision-free path much faster. This new algorithm has been implemented as part of the CATIA/IBM 7565 interface which forms an automatic off-line programming system for the IBM 7565 robot. It has also been implemented as a supervisory collision filter to allow collision-free control of the robot from the operator's console. In both cases the algorithm has been demonstrated to provide efficient and effective collision avoidance for the IBM 7565 robot.     

基于轨迹跟踪车式移动机器人编队控制

针对车式移动机器人的运动学模型特点, 提出一种基于轨迹跟踪多机器人编队控制方法. 首先利用编队结构参数确定队形, 根据编队轨迹和相关参数生成虚拟机器人, 把编队控制转化为跟随机器人对虚拟机器人的轨迹跟踪; 然后运用反步法构造车式移动机器人轨迹跟踪系统的Lyapunov 函数, 通过使该函数负定, 得到跟随机器人的轨迹跟踪控制器; 最后在Microsoft robotics developer studio 4 (MRDS4) 中搭建3D 仿真平台, 设计了3 组实验, 所得结果表明了所提出方法的有效性.     

An on-line robot planning strategy for target interception

On-line planning of gross robot motion for moving-object interception is considered in this article within the context of an active prediction planning, and execution (APPE) strategy. The objective is to find an optimal interception point such that the robot end-effector and the object arrive simultaneously at this target pregrasping point. In this approach, the optimality of the selected rendezvous point on the target trajectory is directly dependent on the robot-trajectory planning technique. Thus, for the solution of the general interception problem, three issues must be addressed: (i) optimal rendezvous-point selection, (ii) optimal robot-trajectory planning, and (iii) replanning in response to gross changes in the predicted target trajectory. The effect of uncertainties in the target-trajectory prediction must be considered at each planning stage. Herein, solutions to the first two problems are briefly reviewed as background to the proposed rendezvous-point replanning strategy. This strategy determines when replanning is necessary, modifies the rendezvous point, and iteratively replans robot “patch trajectories” to new interception points. Simulation results using two different on-line robot-motion-generation strategies are also presented. © 1998 John Wiley & Sons, Inc. 15: 97–114, 1998     

多移动机器人分布式智能避撞规划系统

研究在同一工作环境中多移动机器人的运动规划问题 ,提出将原来比较复杂的大系统问题转化为相对简单的子系统,由各智能机器人依据任务要 求和环境变化,独立调整自身运动状态,完成任务的分布式智能决策体系结构,并给出相应 的模型和算法．     

An offset error compensation method for improving ANN accuracy when used for position control of precision machinery

Artificial Neural Networks (ANNs) have recently become the focus of considerable attention in many disciplines, including robot control, where they can be used as a general class of nonlinear models to solve highly nonlinear control problems. Feedforward neural networks have been widely applied for modelling and control purposes. One of the ANN applications in robot control is for the solution of the inverse kinematic problem, which is important in path planning of robot manipulators. This paper proposes an iterative approach and an offset error compensation method to improve the accuracy of the inverse kinematic solutions by using an ANN and a forward kinematic model of a robot. The offset error compensation method offers potential to generate accurately the inverse solution for a class of problems which have an easily obtained forward model and a complicated solution.Now Lecturing in Taiwan.