Maneuverability modeling and trajectory tracking for fish robot

This study develops a 6-DOF mathematical model for a robotic fish that considers surge, sway, heave, roll, pitch, and yaw. The model considers the conditions of a fish swimming in ocean current perturbations similar to the ocean current perturbations of the slender-body autonomous underwater vehicles. For swimming and turning behaviors, a nonlinear, dynamic, carangiform locomotion model is derived by using a planar four-link model. A 2-DOF barycenter mechanism is proposed to provide body stabilization and to serve as an actuating device for active control design. A barycenter control scheme is developed to change the center of gravity of the robot fish body by moving balancing masses along two axes. The projected torque on x and y axes propel pitch and roll angles to the desired settings. A Stabilizing controller, fish-tail mechanism, rigid body dynamics, and kinematics are incorporated to enable the fish robot to move in three dimensional space. Simulation results have demonstrated maneuverability and control system performance of the developed controller which is proposed to conduct path tracking of the robot fish as it swims under current perturbations.     

On inverting singular kinematics and geodesic trajectory generation for robot manipulators

In this paper, a new method is proposed of solving the inverse kinematic problem for robot manipulators whose kinematics are allowed to possess singularities. The method is based upon the so-called generalized Newton algorithm, introduced by S. Smale, and can be adopted to both nonredundant and redundant kinematics. Moreover, given a pair of points in the external space of a manipulator, the method is capable of generating a minimum-length trajectory joining the points (a geodesic), in particular a straight-line trajectory. Results of representative computer experiments, including those with the PUMA 560 kinematics, are reported in order to illustrate the performance of the method.     

Human like trajectory generation for a biped robot with a four-bar linkage for the knees

The design of a knee joint is a key issue in robotics to improve the locomotion and the performances of the bipedal robots. We study a design for the knee joints of a planar bipedal robot, based on a four-bar linkage. We design walking reference trajectories composed of double support phases, single support phases and impacts. The single support phases are divided in two sub-phases. During the first sub-phase the stance foot has a flat contact with the ground. During the second sub-phase the stance foot rotates on its toes. In the double support phase, both stance feet rotate. This phase is ended by an impact on the ground of the toe of the forward foot, the rear foot taking off. The single support phase is ended by an impact of the heel of the swing foot, the other foot keeping contact with the ground through its toes. A parametric optimization problem is presented for the determination of the parameters corresponding to the optimal cyclic walking gaits. In the optimization process this novel bipedal robot is successively, overactuated (double support with rotation of both stance feet), fully actuated (single support sub-phase with a flat foot contact), and underactuated (single support sub-phase with a rotation of the stance foot). A comparison of the performances with respect to a sthenic criterion is proposed between a biped equipped with four-bar knees and another with revolute joints. Our numerical results show that the performances with a four-bar linkage are bad for the smaller velocities and better for the higher velocities. These numerical results allows us to think that the four-bar linkage could be a good technological way to increase the speed of the future bipedal robots.     

Iterative path-accurate trajectory generation for fast sensor-based motion of robot arms

Sensor-based trajectory generation of industrial robots can be seen as the task of, first, adaptation of a given robot program according to the actually sensed world, and second, its modification that complies with robot constraints regarding its velocity, acceleration, and jerk. The second task is investigated in this paper. Whenever the sensed trajectory violates a constraint, a transient trajectory is computed that, both, keeps the sensed path, and reaches the sensed trajectory as fast as possible while satisfying the constraints. This is done by an iteration of forward scaling and backtracking. In contrast to previous papers, a new backtracking algorithm and an adaptation of the prediction length are presented that are favorable for high-speed trajectories. Arc Length Interpolation is used in order to improve the path accuracy. This is completed by provisions against cutting short corners or omitting of loops in the given path. The refined trajectory is computed within a single sampling step of 4 ms using a standard KUKA industrial robot.     

An effective approach for non-singular trajectory generation of a 5-DOF hybrid machining robot

By taking a newly developed 5-DOF (degree of freedom) hybrid robot as an exemplar, this paper presents a novel and effective approach for non-singular tool trajectory generation. Based upon singularity analysis via inverse kinematics, an algorithm for the C-axis path optimization is developed using the weighted S-curve. Incorporated singular domain detection with the C-axis angle correction, the algorithm can easily be programmed and embedded into the CNC (computer numerical control) system as a postprocessing module and works in real-time. Then, an offline feedrate scheduling method is proposed by considering the drive and geometric accuracy constraints, allowing the rapid yet smooth movement in the neighborhood of singularity to be achieved. Additionally, it is found that the orientation accuracy can be improved by setting adequate cone angle and feedrate value. The results of both simulations and experiments on a prototype machine show that the C-axis can follow the non-singular tool trajectory well with relatively high rotary speed to pass through the singular domain, thereby verifying the effectiveness of the proposed approach.     

Gait generation for a biped robot with knees and torso via trajectory learning and state-transition estimation

The proposed method can generate an optimal feedforward control input and the corresponding optimal walking trajectory minimizing the \(L_2\) norm of the control input by iteration of laboratory experiments. Since a general walking motion involves discontinuous velocity transitions caused by the collision with the ground, the proposed method consists of the combination of a trajectory learning part and an estimation part of the discontinuous state transition mapping using the stored experimental data. We apply the proposed method to a kneed biped robot with a torso, where we also provide a technique to generate an optimal gait not only being energy-efficient but also avoiding the foot-scuffing problem.     

Application of a neural network to the generation of a robot arm trajectory

We propose a neural network model generating a robot arm trajectory. The developed neural network model is based on a recurrent-type neural network (RNN) model calculating the proper arm trajectory based on data acquired by evaluation functions of human operations as the training data. A self-learning function has been added to the RNN model. The proposed method is applied to a 2-DOF robot arm, and laboratory experiments were executed to show the effectiveness of the proposed method. Through experiments, it is verified that the proposed model can reproduce the arm trajectory generated by a human. Further, the trajectory of a robot arm is successfully modified to avoid collisions with obstacles by a self-learning function.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

A trajectory generation method for mobile robot based on iterative extension-like process

In this paper, we propose a trajectory generation method for mobile robot based on iterative extension-like process. Due to use mobile robots in the real world, trajectory generation must be done depending on the faced situation on each occasion. Proposed method enables online iterative trajectory extension process based on a low-order polynomial curve named as trajectory segment. The waypoints on the existing trajectory segment and a waypoint designated every fixed interval are the constraints to trigger the trajectory extension. For maintaining the smooth continuity of the trajectory, the velocity state must be sustained at the connecting point. Resultantly, the trajectory segments are organized into a single smooth trajectory.     

Optimal 3D trajectory generation in delivering missions under urban constraints for a flying robot

Interest in applying flying robots especially quadcopters for civil applications, in particular for delivering purposes, has dramatically grown in the recent years. In fact, since quadcopters are capable of vertical takeoff and landing, they can be widely employed for nearly any aerial task where a human presence is hazardous or response time is critical. In this regard, quadcopters come to be very beneficial in delivering packages; accordingly, generating an optimal flight trajectory plays a preponderant role for meeting this vision. This paper is concerned with generation of a time-optimal 3D path for a quadcopter under municipal restrictions in delivering tasks. To this end, the flying robot’s dynamics is first modeled through Newton–Euler method. Subsequently, the problem is formulated as a time-optimal control problem such that the urban constraints, which are safe-margins of high-rise buildings located throughout the course, are first modeled and then imposed to the trajectory optimization problem as inequality constraints. After discretizing the trajectory by means of Hermit–Simpson method, the optimal control problem is transformed into a nonlinear programming problem and finally is solved by the direct collocation technique. Extensive simulations demonstrate the efficacy of the proposed method and correspondingly verify the effectiveness of the suggested method in generation of optimum 3D routes while all constraints and mission requirements are satisfied.     

Dynamic modeling and control for aerial arm-operating of a multi-propeller multifunction aerial robot

We proposed a multi-propeller multifunction aerial robot that is constructed by a quadrotor with two multi-DOF arms to enable aerial robotic operations. This paper addresses the dynamics and control problems for aerial arm-operation. The dynamic modeling considering the coupling between the arms and main-body subsystems is investigated using the Lagrange approach. The dynamics of the system are partitioned into the main-body dynamics, the arm dynamics, and the interaction dynamics. A composite controller consisting of a main-body sub-controller and an arm sub-controller are presented. Each sub-controller is designed based on the partitioned dynamics. The main-body sub-controller is designed using trajectory linearization control technique. This composite controller is appropriate for real-time implementation due to its simplicity. An optimal planning strategy that minimizes the interaction between main-body subsystem and arm subsystem is proposed. Experimental results are presented, verifying the effectiveness of the composite controller.     

立方体机器人自抗扰平衡控制方法

针对立方体机器人动力学模型多变量、强耦合的问题,提出一种基于自抗扰控制的平衡控制器设计方法.引入虚拟控制量,并在控制量与输出向量之间并行地嵌入多个自抗扰控制器,从而实现对多变量系统的解耦控制,将系统的动态耦合和外部扰动视为各自通道上的自抗扰控制器的总扰动,在为期望姿态安排过渡过程基础上,设计扩张状态观测器对总扰动进行估计并实时补偿.综合采用经验试凑法和带宽法对控制器参数进行整定,对自抗扰控制器系统进行稳定控制、姿态跟踪、抗扰性和鲁棒性实验,并与PID控制系统进行定量对比分析.仿真结果表明,所设计的自抗扰控制器不仅能有效实现立方体机器人的平衡控制,而且较PID控制器具有更好的响应速度、控制精度和强鲁棒性.     

Coverage trajectory planning for a bush trimming robot arm

A novel motion planning algorithm for robotic bush trimming is presented. The algorithm is based on an optimal route search over a graph. Differently from other works in robotic surface coverage, it entails both accuracy in the surface sweeping task and smoothness in the motion of the robot arm. The proposed method requires the selection of a custom objective function in the joint space for optimal node traversal scheduling, as well as a kinematically constrained time interpolation. The algorithm was tested in simulation using a model of the Jaco arm and three target bush shapes. Analysis of the simulated motions showed how, differently from classical coverage techniques, the proposed algorithm is able to ensure high tool positioning accuracy while avoiding excessive arm motion jerkiness. It was reported that forbidding manipulation posture changes during the cutting phase of the motion is a key element for task accuracy, leading to a decrease of the tool positioning error up to 90%. Furthermore, the algorithm was validated in a real‐world trimming scenario with boxwood bushes. A target of 20 mm accuracy was proposed for a trimming result to be considered successful. Results showed that on average 82% of the bush surface was affected by trimming, and 51% of the trimmed surface was cut within the desired level of accuracy. Despite the fact that the trimming accuracy turned out to be lower than the stated requirements, it was found out this was mainly a consequence of the inaccurate, early stage vision system employed to compute the target trimming surface. By contrast, the trimming motion planning algorithm generated trajectories that smoothly followed their input target and allowed effective branch cutting.     

Dynamic modeling and step-climbing analysis of a two-wheeled stair-climbing inverted pendulum robot

ABSTRACT

In this study, the control of a two-wheeled stair-climbing inverted pendulum robot and its climbing motion are analyzed and discussed. The robot adopts a state-feedback controller with a feed-forward constant to stabilize the body and achieve step-climbing motion. The control parameter is considered based on the dynamic model motion on a flat surface and the static model of motion on the step. For climbing stairs with a narrow step tread, a constant torque is applied to reduce the space required for recovering the body stability after climbing. The stability of the robot is numerically analyzed by analyzing the orbital stability of its limit cycle. The stability analysis shows that the control method can achieve a stable stair-climbing motion. The effectiveness of the control method is demonstrated through an experiment. The result indicates that the robot can climb the stairs, and the required time for climbing a single step is approximately 1.8?s.     

Torso motion control and toe trajectory generation of a trotting quadruped robot based on virtual model control

This article presents an intuitive approach based on virtual model control for robust quadrupedal trotting. The controller consists of two main modules: support phase virtual model control for torso motion control and flight phase virtual model control for flight toe trajectory generation. We mapped the relationship between the joint torques of support legs and the torso forces. And virtual forces are applied to the torso to regulate the attitude, height, and velocities of the torso during support phase. To unify the control law, virtual forces are also applied to flight toes to track the planned trajectories that are designed based on lateral velocity of the torso and contact signals of the legs. Moreover, state machine, terrain estimator, and the high level controller are designed to control the robot trotting. Simulations of quadruped trotting versatilely on flat ground, trotting over stairs and slops as well as the impact recovery are reported to demonstrate the effectiveness and robustness of our controller.     

咀嚼机器人的研究现状与发展

本文分析了国内外咀嚼机器人的研究现状，讨论了咀嚼机器人在机械结构、传感系统、控制系统及控制算法等方面的研究内容，然后指出了咀嚼机器人研究中存在的问题。最后，展望了咀嚼机器人的发展方向。     

Rule-based trajectory segmentation for modeling hand motion trajectory

In this paper, we propose a simple but effective method of modeling hand gestures based on the angles and angular change rates of the hand trajectories. Each hand motion trajectory is composed of a unique series of straight and curved segments. In our Hidden Markov Model (HMM) implementation, these trajectories are modeled as a connected series of states analogous to the series of phonemes in speech recognition. The novelty of the work presented herein is that it provides an automated process of segmenting gesture trajectories based on a simple set of threshold values in the angular change measure. In order to represent the angular distribution of each separated state, the von Mises distribution is used. A likelihood based state segmentation was implemented in addition to the threshold based method to ensure that the gesture sets are segmented consistently. The proposed method can separate each angular state of the training data at the initialization step, thus providing a solution to mitigate the ambiguities on initializing the HMM. The effectiveness of the proposed method was demonstrated by the higher recognition rates in the experiments compared to the conventional methods.     

Fuzzy-GA-based trajectory planner for robot manipulators sharing a common workspace

This paper presents a novel fuzzy genetic algorithm (GA) approach to tackling the problem of trajectory planning of two collaborative robot manipulators sharing a common workspace, where the manipulators have to consider each other as a moving obstacle whose trajectory or behaviour is unknown and unpredictable, as each manipulator has individual goals and where both have the same priority. The goals are not restricted to a given set of joint values, but are specified in the workspace as coordinates at which it is desired to place the end-effector of the manipulator. By not constraining the goal to the joint space, the number of possible solutions that satisfies the goal increases according to the number of degrees of freedom of the manipulators. A simple GA planner is used to produce an initial estimation of the movements of the robots' articulations and collision free motion is obtained by the corrective action of the collision-avoidance fuzzy units.     

Evolutionary trajectory planning for an industrial robot

This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE), have been used for the optimization. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.