Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot

The control of a robot system using camera information is a challenging task regarding unpredictable conditions, such as feature point mismatch and changing scene illumination. This paper presents a solution for the visual control of a nonholonomic mobile robot in demanding real world circumstances based on machine learning techniques. A novel intelligent approach for mobile robots using neural networks (NNs), learning from demonstration (LfD) framework, and epipolar geometry between two views is proposed and evaluated in a series of experiments. A direct mapping from the image space to the actuator command is conducted using two phases. In an offline phase, NN–LfD approach is employed in order to relate the feature position in the image plane with the angular velocity for lateral motion correction. An online phase refers to a switching vision based scheme between the epipole based linear velocity controller and NN–LfD based angular velocity controller, which selection depends on the feature distance from the pre-defined interest area in the image. In total, 18 architectures and 6 learning algorithms are tested in order to find optimal solution for robot control. The best training outcomes for each learning algorithms are then employed in real time so as to discover optimal NN configuration for robot orientation correction. Experiments conducted on a nonholonomic mobile robot in a structured indoor environment confirm an excellent performance with respect to the system robustness and positioning accuracy in the desired location.     

On optimal constrained trajectory planning in 3D environments

A novel approach to generating acceleration-based optimal smooth piecewise trajectories is proposed. Given two configurations (position and orientation) in 3D, we search for the minimal energy trajectory that minimizes the integral of the squared acceleration, opposed to curvature, which is widely investigated. The variation in both components of acceleration: tangential (forces on gas pedal or brakes) and normal (forces that tend to drive a car on the road while making a turn) controls the smoothness of generated trajectories. In the optimization process, our objective is to search for the trajectory along which a free moving robot is able to accelerate (decelerate) to a safe speed in an optimal way. A numerical iterative procedure is devised for computing the optimal piecewise trajectory as a solution of a constrained boundary value problem. The resulting trajectories are not only smooth but also safe with optimal velocity (acceleration) profiles and therefore suitable for robot motion planning applications. Experimental results demonstrate this fact.     

Hierarchical incremental path planning and situation-dependent optimized dynamic motion planning considering accelerations.

This paper studies a hierarchical approach for incrementally driving a nonholonomic mobile robot to its destination in unknown environments. The A* algorithm is modified to handle a map containing unknown information. Based on it, optimal (discrete) paths are incrementally generated with a periodically updated map. Next, accelerations in varying velocities are taken into account in predicting the robot pose and the robot trajectory resulting from a motion command. Obstacle constraints are transformed to suitable velocity limits so that the robot can move as fast as possible while avoiding collisions when needed. Then, to trace the discrete path, the system searches for a waypoint-directed optimized motion in a reduced 1-D translation or rotation velocity space. Various situations of navigation are dealt with by using different strategies rather than a single objective function. Extensive simulations and experiments verified the efficacy of the proposed approach.     

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.     

基于速度空间的移动机器人同时避障和轨迹跟踪方法

针对有障碍物环境下非完整轮式 移动机器人的轨迹跟踪问题,提出一种基于速度空间的同时避障和轨迹跟踪方法(VSTTM).首先,根据机器人 的动力学特性构建速度空间,得到由速度元组构成的控制集;然后,构造目标函数并对各控制量进行 评价,其中跟踪误差评价函数评估跟踪效果,碰撞检测函数检测是否发生碰撞,终端状态惩罚项保证 算法的稳定性;最后,通过优化过程找到最优的无碰控制量.仿真结果表明了所提出方法的有效性.     

Coordinated control method of space-tethered robot system for tracking optimal trajectory

Space-tethered robot system is a new kind of space robot, which consists of a robot platform, space tether, and operation robot. This paper presents the coordinated control method in order to save thruster fuel of operation robot in the process of tracking the optimal approach trajectory. First, the optimal approach trajectory of an operation robot is designed using the Gauss pseudospectral method, which resulted in continuous optimal control force using the Lagrange interpolation scheme. The optimal control force is optimized and distributed to space tether and thrusters through simulated annealing algorithm in discrete points, which minimized fuel consumption of thrusters. The distributive continuous force is obtained via cubic polynomial fitting of optimal distributive force in 0.1s discrete time point. To tracking the optimal trajectory, Fuzzy Proportional-Derivative controller is designed with the help of optimal distribution force which come from optimization model. Simultaneously, the relative attitude of the operation robot is stabilized using attitude time-delay algorithm through the reaction wheels. Numerical results are presented, demonstrating the validity of saving thruster fuel and well performance in tracking the optimal trajectory.

     

A novel offline programming approach of robot welding for multi-pipe intersection structures based on NSGA-Ⅱ and measured 3D point-clouds

The multi-pipe intersection structure in form of co-main pipe is widely used in various industries. To improve its welding quality and efficiency, this paper is devoted to proposing an offline programming approach to the robot trajectory based on NSGA-Ⅱ and measured 3D point-clouds. First, considering the existence of deviation between the actual workpiece and its ideal model, this paper selects the actual 3D point-cloud of weld seam as the research object and extracts its feature points by combining the characteristic of the spatial curve, which can reduce the data density while preserving their geometric characteristics. Second, to ensure the continuity of motion parameters while taking the calculation and fitting accuracy into account, the cubic NURBS is applied to fit the actual weld position, and a fast-adaptive fitting nodes configuration scheme is designed according to the variation characteristics of the spatial curve and the fitting error restriction. Third, this paper introduces a trajectory adaptive discretization method based on the chord error constraint for robot program generation, and establishes an optimization model of the robot welding trajectory based on NSGA-Ⅱ frame, which gives the robot joint's motion trajectories with optimal welding quality and cable twisting fluctuation. Finally, the experiments are designed to verify the correctness of the aforementioned approach.     

带有未知参数和有界干扰的移动机器人轨迹跟踪控

针对模型参数未知和存在有界干扰的非完整移动机器人的轨迹跟踪控制问题,本文提出了一种鲁棒自适应轨迹跟踪控制器方法.非完整移动机器人的控制难点在于它的运动学系统是欠驱动的.针对这一难点,本文利用横截函数的思想,引入新的辅助控制器,使得非完整移动机器人系统不再是一个欠驱动系统,缩减了控制器设计的难度,进而利用非线性自适应算法和参数映射方法构造李雅谱诺夫函数.通过李雅普诺夫方法设计控制器和参数自适应器,从而使得非完整移动机器人的跟随误差任意小,即可以任意小的误差来跟随任意给定的参考轨迹.仿真结果证明了方法的有效性.     

Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot

The purpose of this paper is to propose a hybrid trigonometric compound function neural network (NN) to improve the NN-based tracking control performance of a nonholonomic mobile robot with nonlinear disturbances. In the mobile robot control system, two NN controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the tracking control of the mobile robot. The neuron functions of the hidden layer in the three-layer feedforward network structure consist of the compound cosine function and the compound sine function combining a cosine or a sine function with a unipolar sigmoid function. The main advantages of this NN-based mobile robot control system are better real-time control capability and control accuracy by use of the proposed NN controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances of dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of a nonholonomic mobile robot has better real-time control capability and control accuracy than the compound cosine function NN control system of a nonholonomic mobile robot and then verify the effectiveness of the proposed hybrid trigonometric compound function NN controller for improving the tracking control performance of a nonholonomic mobile robot with nonlinear disturbances.     

From Trajectory Tracking Control to Leader–Follower Formation Control

Abstract

This work investigates the leader–follower formation control of multiple nonholonomic mobile robots. First, the formation control problem is converted into a trajectory tracking problem and a tracking controller based on the dynamic feedback linearization technique drives each follower robot toward its corresponding reference trajectory in order to achieve the formation. The desired orientation for each follower is selected such that the nonholonomic constraint of the robot is respected, and thus the tracking of the reference trajectory for each follower is feasible. An adaptive dynamic controller that considers the actuators dynamics in the design procedure is proposed. The dynamic model of the robots includes the actuators dynamics in order to obtain the velocities as control inputs instead of torques or voltages. Using Lyapunov control theory, the tracking errors are proven to be asymptotically stable and the formation is achieved despite the uncertainty of the dynamic model parameters. In order to assess the proposed control laws, a ROS-framework is developed to conduct real experiments using four ROS-enabled mobile robots TURTLEBOTs. Moreover, the leader fault problem, which is considered as the main drawback of the leader–follower approach, is solved under ROS. An experiment is conducted where in order to overcome this problem, the desired formation and the leader role are modified dynamically during the experiment.