Collision-free control of an omni-directional vehicle

This study addresses the problem of controlling an omni-directional vehicle with both state and control dependent constraints. The task of the vehicle is to attain its desired final position given in the task space. The control constraints resulting from the physical abilities of actuators driving the vehicle wheels are also taken into account during the robot movement. The problem of collision avoidance is solved here based on an exterior penalty function approach which results in smooth vehicle velocities near obstacles. Provided that, a solution to the aforementioned vehicle task exists, the Lyapunov stability theory is used to derive the control scheme. The numerical simulation results carried out for the omni-directional vehicle operating in both a constraint-free task space and task space including obstacles, illustrate the performance of the proposed controllers.     

Control-Based Solution to Inverse Kinematics for Mobile Manipulators Using Penalty Functions

This study offers the solution at the control feedback level to the inverse kinematics problem subject to state equality and inequality constraints for mobile manipulators. Based on the Lyapunov stability theory, a class of controllers generating the mobile manipulator trajectory whose attractor attained in a finite time, fulfills the above state constraints. The problem of both holonomic manipulability enforcement and collision avoidance is solved here based on an exterior penalty function approach which results in continuous mobile manipulator velocities near obstacles. The numerical simulation results carried out for a mobile manipulator consisting of a nonholonomic wheel and a holonomic manipulator of two revolute kinematic pairs, operating in both a constraint-free task space and task space including obstacles, illustrate the performance of the proposed controllers.     

A Sequential Bi-criteria Search Algorithm for Robot Path Planning in the Box Pushing Problem

The collision-free trajectory planning method subject to control constraints for mobile manipulators is presented. The robot task is to move from the current configuration to a given final position in the workspace. The motions are planned in order to maximise an instantaneous manipulability measure to avoid manipulator singularities. Inequality constraints on state variables i.e. collision avoidance conditions and mechanical constraints are taken into consideration. The collision avoidance is accomplished by local perturbation of the mobile manipulator motion in the obstacles neighbourhood. The fulfilment of mechanical constraints is ensured by using a penalty function approach. The proposed method guarantees satisfying control limitations resulting from capabilities of robot actuators by applying the trajectory scaling approach. Nonholonomic constraints in a Pfaffian form are explicitly incorporated into the control algorithm. A computer example involving a mobile manipulator consisting of nonholonomic platform (2,0) class and 3DOF RPR type holonomic manipulator operating in a three-dimensional task space is also presented.     

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.     

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.     

A new potential field-based algorithm for path planning

In this paper, the path-planning problem is considered. We introduce a new potential function for path planning that has the remarkable feature that it is free from any local minima in the free space irrespective of the number of obstacles in the configuration space. The only global minimum is the goal configuration whose region of attraction extends over the whole free space. We also propose a new method for path optimization using an expanding sphere that can be used with any potential or penalty function. Simulations using a point mobile robot and smooth obstacles are presented to demonstrate the qualities of the new potential function. Finally, practical considerations are also discussed for nonpoint robots     

Multi-agent consensus algorithm with obstacle avoidance via optimal control approach

Multi-agent consensus problem in an obstacle-laden environment is addressed in this study. A novel optimal control approach is proposed for the multi-agent system to reach consensus as well as avoid obstacles with a reasonable control effort. An innovative nonquadratic penalty function is constructed to achieve obstacle avoidance capability from an inverse optimal control perspective. The asymptotic stability and optimality of the consensus algorithm are proven. In addition, the optimal control law only requires local information from the communication topology to guarantee the proposed behaviour, rather than all agents’ information. The consensus and obstacle avoidance are validated through various simulations.     

多智能体系统输入约束下的一致性与轨迹规划研究

针对多智能体系统提出了一种分布式预测控制方法. 首先, 研究了有输入约束下的一致性问题. 其次, 对环境中有障碍物的多智能体轨迹规划进行了研究, 其中只有当障碍物进入智能体有限感知区域内时, 障碍物状态信息才能被获取. 基于预测控制方法, 设计了一种分布式控制算法来解决上面两个问题. 构造一个与每个智能体动力学相交互的代价函数, 设计相应最优控制问题, 从而实现优化控制算法. 智能体间交互信息是其邻居在上一时刻的最优控制状态. 系统稳定性可以通过构造代价函数中的一个终点状态控制器与最优控制问题中的一个终点状态区域来保证. 仿真研究表明所提方法的有效性.     

Neural network–based reconfiguration control for spacecraft formation in obstacle environments

This paper proposes an adaptive formation reconfiguration control scheme based on the leader‐follower strategy for multiple spacecraft systems. By taking the predesigned desired velocities and the trajectories as reference signals, a set of coordination tracking controllers is constructed by combining the reconstructed dynamic system and the neural network–based reconfiguration algorithm together. To avoid collisions between spacecraft and obstacles during the formation configuration process, the null space–based behavioral control is integrated into the control design. Since the spacecraft dynamics contains unknown nonlinearity and disturbance, it is challenging to make the system robust to uncertainties and improve the control precision simultaneously. To solve this problem, both the adaptive neural network strategy and the finite‐time control theory are employed. Finally, 2 simulation examples are carried out to verify the proposed algorithm, showing that the formation reconfiguration task can be executed successfully while achieving high control precision.     

Motion Control of a Nonholonomic Mobile Manipulator in Task Space

In this paper, the motion control of a mobile manipulator subjected to nonholonomic constraints is investigated. The control objective is to design a computed‐torque controller based on the coupled dynamics of the mobile manipulator. The proposed controller achieves the capability of simultaneous tracking of a reference velocity for the mobile base and a reference trajectory for the end‐effector. The aforementioned reference velocity and trajectory are defined in the task space, such task setting imitates the actual working conditions of a mobile manipulator and thus makes the control problem practical. To solve this tracking problem, a steering velocity is firstly designed based on the first‐order kinematic model of the nonholonomic mobile base via dynamic feedback linearization. The main merit of the proposed steering velocity design is that it directly utilizes the reference velocity set in the task space without requiring the knowledge of a reference orientation. A torque controller is subsequently developed based on a proposed Lyapunov function which explicitly considers the coupled dynamics of the mobile manipulator to ensure the mobile base and end‐effector track the reference velocity and trajectory respectively. This proposed computed‐torque controller is able to realize asymptotic stability of both the base velocity tracking error and the end‐effector motion tracking error. Simulations are conducted to demonstrate the effectiveness of the proposed controller.     

Formation and obstacle avoidance control for multiagent systems

This paper considers the problems of formation and obstacle avoidance for multiagent systems.The objective is to design a term of agents that can reach a desired formation while avoiding collision with obstacles.To reduce the amount of information interaction between agents and target,we adopt the leader-follower formation strategy.By using the receding horizon control (RHC),an optimal problem is formulated in terms of cost minimization under constraints.Information on obstacles is incorporated online as sensed in a limited sensing range.The communication requirements between agents are that the followers should obtain the previous optimal control trajectory of the leader to each update time.The stability is guaranteed by adding a terminal-state penalty to the cost function and a terminal-state region to optimal problem.Finally,simulation studies are provided to verify the effectiveness of the proposed approach.     

An improved differential evolution algorithm for the task assignment problem

An improved differential evolution algorithm (IDE) is proposed to solve task assignment problem. The IDE is an improved version of differential evolution algorithm (DE), and it modifies two important parameters of DE algorithm: scale factor and crossover rate. Specially, scale factor is adaptively adjusted According to the objective function values of all candidate solutions, and crossover rate is dynamically adjusted with the increasement of iterations. The adaptive scale factor and dynamical crossover rate are combined to increase the diversity of candidate solutions, and to enhance the exploration capacity of solution space of the proposed algorithm. In addition, a usual penalty function method is adopted to trade-off the objective and the constraints. Experimental results demonstrate that the optimal solutions obtained by the IDE algorithm are all better than those obtained by the other two DE algorithms on solving some task assignment problems.     

Set-point Control of Mobile Robot with Obstacle Detection and Avoidance Using Navigation Function - Experimental Verification

This paper presents the results of an experimental verification of mobile robot control algorithm including obstacle detection and avoidance. The controller is based on the navigation potential function that was proposed in work (Urakubo, Nonlinear Dyn. 81(3), 1475–1487 2015). Conducted experiments considered the task of reaching and stabilization of robot in point. The navigation potential agregates information of robot position and orientation but also the repelling potentials of obstacles. The obstacle detection is performed solely with the use of laser scanner. The experiments show that the method can easily handle environments with one or two obstacles even if they instantly hide or show-up due to the scanner range limits. The experiments also indicate that the utilized control method has a good potential for being used in parallel parking task.     

基于观测优化的双机协同控制与避障

为实现不确定环境下无人机对远程超视距目标的精确指示,考虑空中移动和静止障碍物,提出基于观测优化的双机协同控制与避障算法,从而增加测量信息,减小目标状态估计的不确定度.以费舍尔信息矩阵(FIM)表征所获取的目标信息,理论推导出三维空间中双机最优观测的指标函数,并设计无人机协同控制律,得到优化的无人机观测航迹,增强无人机协...     

Joint trajectory generation for redundant robots in an environment with obstacles

Collision avoidance is an absolutely essential requirement for a robot to complete a task in an environment with obstacles. For kinematically redundant robots, collision avoidance can be achieved by making full use of the redundancy. In this article, the problem of determining collision-free joint space trajectories for redundant robots in an environment with multiple obstacles is considered, and the “command generator” approach is employed to generate such trajectories. In this approach, a nondifferentiable distance objective function is defined and is guaranteed to increase wherever possible along the trajectory through a vector in N(J), the null space of Jacobian matrix J. Algorithms that implement this nondifferentiable optimization problem are fully developed. It is shown that the proposed collision-free trajectory generation scheme is efficient and practical. Extensive simulation results of a four-link robot example are presented and analyzed.     

An Adaptive Regulator of Robotic Manipulators in the Task Space

This note addresses the problem of position control of robotic manipulators both nonredundant and redundant in the task space. A computationally simple class of task space regulators consisting of a transpose adaptive Jacobian controller plus an adaptive term estimating generalized gravity forces is proposed. The Lyapunov stability theory is used to derive the control scheme. The conditions on controller gains ensuring asymptotic stability are obtained herein in a form of simple inequalities including some information extracted from both robot kinematic and dynamic equations. The performance of the proposed control strategy is illustrated through computer simulations for a direct-drive arm of a SCARA type redundant manipulator with the three revolute kinematic pairs operating in a two-dimensional task space.     

Feedback control for robotic manipulator with an uncertain Jacobian matrix

In most applications of robots, a desired path for the end‐effector is usually specified in task space such as Cartesian space. One way to move the robot along this path is to solve the inverse kinematics problem to generate the desired angles in joint space. However, it is a very time consuming task to solve the inverse kinematics problem. Furthermore, in the presence of uncertainty in kinematics, it is impossible to derive the desired joint angle from the desired end‐effector path and the Jacobian matrix of the mapping from joint space to task space. In this article, a feedback control law using an uncertain Jacobian matrix is proposed for setpoint control of robots. Sufficient conditions for the bound of the estimated Jacobian matrix and stability conditions for the feedback gains are presented to guarantee the stability and passivity of the robots. A gravity regressor with an uncertain Jacobian matrix is also proposed for gravitational force compensation when the gravitational force is uncertain. Simulation results are presented to illustrate the performance of the proposed controllers. ©1999 John Wiley & Sons, Inc.     

三维动态环境下多无人机编队分布式保持控制

针对无人机编队沿参考轨迹飞行时遭遇突发障碍物而发生碰撞的问题, 提出一种可实时避障及机间避碰的分布式编队保持算法. 基于虚拟结构编队策略, 采用非线性模型预测控制(NMPC) 方法设计分布式编队控制器. 为了实现通讯延迟下的机间避碰, 采用基于不同优先级的改进避碰惩罚策略. 仿真结果表明, 所设计的分布式编队控制器能保证编队及时避开环境中的突发障碍物, 且无人机间不发生互碰, 避障后的各编队继续以原队形沿参考轨迹飞行.

     

一种工业过程稳态优化控制算法

针对带有输出关联约束的工业过程,提出一种确定其稳态优化控制的算法.首先通过对数变换将原问题转化为一个等价而且可在对数空间求解的优化控制问题;然后为避免事先选择一个合适罚系数的困难,在算法中引入了目标函数的线性化形式.该优化算法不仅能收敛到正确的系统最优解,而且可用现有的二次规划算法计算.应用简单的滤波技术,改善了算法在有量测噪声情况下的性能.仿真结果表明,所提出的优化算法是有效的.     

Correlation between learning (probability of success) and fuzzy entropy in control of intelligent robot's part macro-assembly tasks with sensor fusion techniques

A correlation between a learning and a fuzzy entropy, using the control of robotic part macro-assembly (part-bringing) task as an example, is introduced. Two intelligent part-bringing algorithms, to bring a part from an initial position to an assembly hole or a receptacle (target or destination) for a purpose of a part mating in a partially unknown environment containing obstacles, related to a robotic part assembly task are introduced. An entropy function, which is a useful measure of the variability and the information in terms of uncertainty, is introduced to measure its overall performance of a task execution related to the part-bringing task. The degree of uncertainty associated with the part-bringing task is used as an optimality criterion, e.g. minimum entropy, for a specific task execution. Fuzzy set theory, well-suited to the management of uncertainty, is used to address the uncertainty associated with the macro-assembly procedure. In the first algorithm, a macro-assembly, locating various shaped assembly holes (targets) in the workspace corresponding to the shapes of the parts and then bringing the part to the corresponding target, despite existing obstacles is introduced. This is accomplished by combining a neural network control strategy coordinating with a mobile rectilinear grid composed of optical sensors as well as fuzzy optimal controls. Depending on topological relationships among the part's present position, the position of obstacles, and the target position in the workspace, a specific rulebase from a family of distinct fuzzy rulebases for avoiding obstacles is activated. The higher the probability, the input pattern (or value) of the neural network to be identified as the desired output is, the lower the fuzzy entropy is. Through the fuzzy entropy, a degree of identification between the input pattern and the desired output of the neural network can be measured. In the second algorithm, a macro-assembly with a learning algorithm and a sensor fusion for bringing the part to the target is introduced. By employing a learning approach, the uncertainty associated with the part-bringing task is reduced. The higher the probability of success is, the lower the fuzzy entropy is. The results show clearly the correlation between a probability of success related to the task execution of the part-bringing and the fuzzy entropy, and also show the effectiveness of above methodologies. The proposed technique is not only a useful tool to measure the behaviour of the learning but applicable to a wide range of robotic tasks including motion planning, and pick and place operations with various shaped parts and targets.