ROBUST NEURAL NETWORK CONTROL OF RIGID LINK FLEXIBLE-JOINT ROBOTS

A robust Neural Network (NN) controller is proposed for the motion control of rigid-link flexible-joint (RLFJ) robots. No weak joint elasticity assumption is needed. The NNs are used to approximate three very complicated nonlinear functions. Our NN approach requires no off-line learning phase, and no lengthy and tedious preliminary analysis to find the regression matrices. Most importantly, we can guarantee the uniformly ultimately bounded (UUB) stability of tracking errors and NN weights. The controller can be regarded as a universal reusable controller because the same controller can be directly applied to different RLFJ robots with different masses and lengths within the same class, for instance, of two-link revolute RLFJ robots.     

Adaptive control of nonlinear systems: A case study of underwater robotic systems

The problem of controlling underwater mobile robots in 6 degrees of freedom (DOF) is addressed. Underwater mobile robots where the number of thrusters and control surfaces exceeds the number of controllable DOF are considered in detail. Unlike robotic manipulators underwater mobile robots should include a velocity dependent thruster configuration matrix B( q ), which modifies the standard manipulator equation to: Mq + C( q ) q + g(x) = B( q ) u where x = J( x ) q . Uncertainties in the thruster configuration matrix due to unmodeled nonlinearities and partly known thruster characteristics are modeled as multiplicative input uncertainty. This article proposes two methods to compensate for the model uncertainties: (1) an adaptive passivity-based control scheme and (2) deriving a hybrid (adaptive and sliding) controller. The hybrid controller combines the adaptive scheme where M, C, and g are estimated on-line with a switching term added to the controller to compensate for uncertainties in the input matrix B. Global stability is ensured by applying Barbalat's Lyapunov-like lemma. The hybrid controller is simulated for the horizontal motion of the Norwegian Experimental Remotely Operated Vehicle (NEROV).     

Performance of a class of multi-robot deploy and search strategies based on centroidal voronoi configurations

This article considers a class of deploy and search strategies for multi-robot systems and evaluates their performance. The application framework used is deployment of a system of autonomous mobile robots equipped with required sensors in a search space to gather information. The lack of information about the search space is modelled as an uncertainty density distribution. The agents are deployed to maximise single-step search effectiveness. The centroidal Voronoi configuration, which achieves a locally optimal deployment, forms the basis for sequential deploy and search (SDS) and combined deploy and search (CDS) strategies. Completeness results are provided for both search strategies. The deployment strategy is analysed in the presence of constraints on robot speed and limit on sensor range for the convergence of trajectories with corresponding control laws responsible for the motion of robots. SDS and CDS strategies are compared with standard greedy and random search strategies on the basis of time taken to achieve reduction in the uncertainty density below a desired level. The simulation experiments reveal several important issues related to the dependence of the relative performances of the search strategies on parameters such as the number of robots, speed of robots and their sensor range limits.     

Motion coordination and reactive control of autonomous multi-manipulator systems

The emerging field of service robots demands new systems with increased flexibility. The flexibility of a robot system can be increased in many different ways. Mobile manipulation—the coordinated use of manipulation capabilities and mobility—is an approach to increase robots flexibility with regard to their motion capabilities. Most mobile manipulators that are currently under development use a single arm on a mobile platform. The use of a two-arm manipulator system allows increased manipulation capabilities, especially when large, heavy, or non-rigid objects must be manipulated. This article is concerned with motion control for mobile two-arm systems. These systems require new schemes for motion coordination and control. A coordination scheme called transparent coordination is presented that allows for an arbitrary number of manipulators on a mobile platform. Furthermore, a reactive control scheme is proposed to enable the platform to support sensor-guided manipulator motion. Finally, this article introduces a collision avoidance scheme for mobile two-arm robots. This scheme surveys the vehicle motion to avoid platform collisions and arm collisions caused by self-motion of the robot. © 1996 John Wiley & Sons, Inc.     

A parallel approach of the inverse kinematics solutions for robots using a transputer network

This article presents a parallel method for computing inverse kinematics solutions for robots with closed-form solutions moving along a straight line trajectory specified in Cartesian space. Zhang and Paul's approach¹ is improved for accuracy and speed. Instead of using previous joint positions as proposed by Zhang and Paul, a first order prediction strategy is used to decouple the dependency between joint positions, and a zero order approximation solution is computed. A compensation scheme using Taylor series expansion is applied to obtain the trajectory gradient in joint space to replace the correction scheme proposed by Zhang and Paul. The configuration of a Mitsubishi RV-M1 robot is used for the simulation of a closed-form inverse kinematics solutions. An Alta SuperLink/XL with four transputer nodes is used for parallel implementation. The simulation results show a significant improvement in displacement tracking errors and joint configuration errors along the straight line trajectory. The computational latency is reduced as well. The modified approach proposed in this work is more accurate and faster than Zhang and Paul's approach for robots with closed-form inverse kinematics solutions. © 1996 John Wiley & Sons, Inc.     

Methodology of Concept Control Synthesis to Avoid Unmoving and Moving Obstacles

The dynamic path generation problem of robots in environments with other unmoving and moving objects is considered. Generally, the problem is known in the literature as find path or robot motion planning. In this paper, we apply the behavioral cloning approach to design the robot controller. In behavioral cloning, the system learns from control traces of a human operator. The task for the given problem is to find a controller in the form of an explicit mathematical expression. Thus, machine learning programs to induce the operator's trajectories as a set of symbolic constraints are used. Then, mathematical induction to generalize the obtained equations in order to apply them in situ with an infinite number of obstacles is also used. A method to evaluate cloning success is proposed. The typical kind of noise is included.     

INTELLIGENT MOTION CONTROL FOR OMNIDIRECTIONAL MOBILE ROBOTS USING ANT COLONY OPTIMIZATION

This article presents an intelligent system-on-a-programmable-chip-based (SoPC) ant colony optimization (ACO) motion controller for embedded omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. Both ACO parameter autotuner and kinematic motion controller are integrated in one field-programmable gate array (FPGA) chip to efficiently construct an experimental mobile robot. The optimal parameters of the motion controller are obtained by minimizing the performance index using the proposed SoPC-based ACO computing method. These optimal parameters are then employed in the ACO-based embedded kinematic controller in order to obtain better performance for omnidirectional mobile robots to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed intelligent ACO-based embedded controller for omnidirectional mobile robots. These results indicate that the proposed ACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm (GA) optimal controllers.     

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.     

Child Perception of Humanoid Robot Appearance and Behavior

This study investigated children’s attitudes toward humanoid robots that exhibit various anthropomorphic appearances and behaviors. A total of 578 children aged from 8 to 14 years were recruited to evaluate humanoid robots depicted either in still images (N = 267) or videos (N = 311). The results showed that the degree of anthropomorphism affected children’s attitudes toward the robots. An uncanny valley was observed in this study, indicating that children prefer robots created with a moderate level of human likeness over those that have a highly human-like appearance but remain distinguishable from humans. A striking finding was that moving robots exhibiting social cues moderate the uncanny valley plot, thus contradicting Mori’s uncanny valley hypothesis, which posits that emotional responses are greater for moving robots than for static robots. In addition, the children in this study perceived the robots as more socially and physically attractive when the robots exhibited social cues. In summary, the current results suggest that children prefer moderately realistic robots and that robot behavior is a key determinant of how children perceive robots. A moderate level of anthropomorphic appearance combined with appropriate social cues can enhance child preferences for and acceptance of robots.     

Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots

This paper presents a novel method of integrating fuzzy logic (FL) and genetic algorithm (GA) to solve the simultaneous localization and mapping (SLAM) problem of mobile robots. The core of the proposed SLAM algorithm is based on an island model GA (IGA) which searches for the most probable map(s) such that the associated pose(s) provides the robot with the best localization information. Prior knowledge about the problem domain is transferred to GA in order to speed up the convergence. Fuzzy logic is employed to serve this purpose and allows the IGA to conduct the search starting from a potential region of the pose space. The underlying fuzzy mapping rules infer the uncertainty in the robot's location after executing a motion command and generate a sample-based prediction of its current position. This sample set is used as the initial population for the proposed IGA. Thus the GA-based search starts with adequate knowledge on the problem domain. The correspondence problem in SLAM is solved by exploiting the property of natural selection, which supports better performing individuals to survive in the competition. The proposed algorithm follows essentially no assumption about the environment and has the capacity to resolve the loop closure problem without maintaining explicit loop closure heuristics. The algorithm processes sensor data incrementally and therefore, has the capability of real time map generation. Experimental results in different indoor environments are presented to validate robustness of the algorithm.     

Mobile robot team forming for crystallization of proteins

The process of protein crystallization is explained using the theory of robotics, particularly path planning of mobile robots. Path planning is a procedure which specifies motion trajectories of multiple mobile robots to form a robotic team with a desired pattern. Since protein crystals consist of a large number of protein molecules which come together to form a 3D lattice of uniform structure, it is hypothesized that each protein behaves like a mobile robot and takes adequate path to form a robotic team (crystal). Based on this hypothesis, it is shown that trajectories of the proteins should be simple and local, which generates three rules of motion for the protein robots, i.e., (a) each protein searches for its nearest neighbor, (b) each protein takes the shortest path to approach the nearest neighbor, and (c) multiple proteins may form a sub-team of proteins. It is then proven mathematically that the planned path according to the three rules is stable and able to crystallize the proteins. Interaction forces at the molecular level are analyzed to show that the simple and local motion of the proteins is physically warranted. Computer simulation and experimental results are presented to validate the new theory.

Virtual Force/Tactile Sensors for Interactive Machines Using the User's Biological Signals

This study proposes a new approach to virtual realization of force/tactile sensors in machines equipped with no real sensors. The key of our approach is that a machine exploits the user's biological signals. Therefore, this approach is not dependent on controlled objects and is expected to be widely applicable for a variety of machines including robots. This article describes an example robotic system comprised of an industrial robot manipulator, a motion capture system and a surface electromyogram (EMG) measurement apparatus. By monitoring/recording the user's surface EMG and postural information in real-time, we show that a robot equipped with no force/tactile sensors behaved similarly to one possessing sensors over its body. Another advantage of our approach is demonstrated by a task in which a robot and a user cooperatively hold and move a heavy load.     

Detection of robustly collision-free trajectories in unpredictable environments in real-time

One of the ultimate goals in robotics is to make robots of high degrees of freedom (DOF) work autonomously in real world environments. However, real world environments are unpredictable, i.e., how the objects move are usually not known beforehand. Thus, whether a robot trajectory is collision-free or not has to be checked on-line based on sensing as the robot moves. Moreover, in order to guarantee safe motion, the motion uncertainty of the robot has to be taken into account. This paper introduces a general approach to detect if a high-DOF robot trajectory is continuously collision-free even in the presence of robot motion uncertainty in an unpredictable environment in real time. Our method is based on the novel concept of dynamic envelope, which takes advantage of progressive sensing over time without predicting motions of objects in an environment or assuming specific object motion patterns. The introduced approach can be used by general real-time motion planners to check if a candidate robot trajectory is continuously and robustly collision-free (i.e., in spite of uncertainty in the robot motion).     

Optimal assignment of robot tasks with precedence for muliti-robot coordination by disjunctive graphs and state-space search

An approach to optimal assignment of tasks with precedence relationships to multiple robots is proposed. The robots are assumed to share a common workspace and work cooperatively to accomplish a given process plan consisting of a set of tasks. The optimal task assignment is defined to be the one that results in spending the least amount of time to complete the plan under the criterion that no robot collision will occur when the assigned tasks are performed. The ordering of the tasks in the process plan is described by a topological tree, which is then expanded to form a larger state-space tree without redundant tree paths. Each path in the expanded tree represents a partially developed assignment of the tasks to the robots, and a graph formulation scheme is presented for estimating the cost of the assignment. A collision-free motion schedule for each robot based on each task assignment can be obtained by finding the minimaximal path in a disjunctive graph formulated by the scheme. By using the A^* algorithm, a search method for finding the optimal assignment with the minimum cost is presented. Some heuristic rules are also proposed to speed up the search process. Simulation results are illustrated to show the effectiveness of the proposed approach. © 1995 John Wiley & Sons, Inc.     

Optimal Trajectory Planning for Wheeled Mobile Robots Based on Kinematics Singularity

This research introduces a new optimality criterion for motion planning of wheeled mobile robots based on a cost index that assesses the nearness to singularity of forward and inverse kinematic models. Slip motions, infinite estimation error and impossible control actions are avoided escaping from singularities. In addition, high amplification of wheel velocity errors and high wheel velocity values are also avoided by moving far from the singularity. The proposed cost index can be used directly to complement path-planning and motion-planning techniques (e.g. tree graphs, roadmaps, etc.) in order to select the optimal collision-free path or trajectory among several possible solutions. To illustrate the applications of the proposed approach, an industrial forklift, equivalent to a tricycle-like mobile robot, is considered in a simulated environment. In particular, several results are validated for the proposed optimality criterion, which are extensively compared to those obtained with other classical optimality criteria, such as shortest-path, time-optimal and minimum-energy.     

基于遗传算法的欠驱动机器人运动规划

研究了平面欠驱动机器人的非完整运动规划问题,建立欠驱动机器人系统动力学模型,提出了一种利用遗传算法(GA)解决欠驱动机器人运动规划的方法。引入部分稳定控制器的思想,提出基于能量最优的评价函数,利用遗传算法对评价函数进行离线优化,得到有关部分稳定控制器的切换规则。此方法可以推广到任意平面欠驱动机器人的规划问题上,以平面3R欠驱动机器人为研究对象进行了数值仿真,仿真结果验证了该方法的有效性。     

Development of a configuration space motion planner for robot in dynamic environment

In this paper, the on-line motion planning of articulated robots in dynamic environment is investigated. We propose a practical on-line robot motion planning approach that is based upon pre-computing the global configuration space (C-space) connectivity with respect to all possible obstacle positions. The proposed motion planner consists of an off-line stage and an on-line stage. In the off-line stage, the obstacles in the C-space (C-obstacle) with respect to the obstacle positions in the workspace are computed, which are then stored using a hierarchical data structure with non-uniform 2^m trees. In the on-line stage, the real obstacle cells in the workspace are identified and the corresponding 2^m trees from the pre-computed database are superposed to construct the real-time C-space. The collision-free path is then searched in this C-space by using the A* algorithm under a multi-resolution strategy which has excellent computational efficiency. In this approach, the most time-consuming operation is performed in the off-line stage, while the on-line computing only need to deal with the real-time obstacles occurring in the dynamic environment. The minimized on-line computational cost makes it feasible for real-time on-line motion planning. The validity and efficiency of this approach is demonstrated using manipulator prototypes with 5 and 7 degree-of-freedom.     

Integrating human observer inferences into robot motion planning

Our goal is to enable robots to produce motion that is suitable for human–robot collaboration and co-existence. Most motion in robotics is purely functional, ideal when the robot is performing a task in isolation. In collaboration, however, the robot’s motion has an observer, watching and interpreting the motion. In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator. We formalize predictability and legibility as properties of motion that naturally arise from the inferences in opposing directions that the observer makes, drawing on action interpretation theory in psychology. We propose models for these inferences based on the principle of rational action, and derive constrained functional trajectory optimization techniques for planning motion that is predictable or legible. Finally, we present experiments that test our work on novice users, and discuss the remaining challenges in enabling robots to generate such motion online in complex situations.     

Optimal motion coordination of two robots—a polynomial parameterization approach to trajectory resolution

A novel approach is presented for the optimal coordination of two robots performing continuous-path manufacturing tasks. Two types of operations are considered: contact operations, such as deburring, which require the tool to maintain contact with the workpiece during the operation execution, or non-contact operations, such as arc-welding, where the tool moves relative to the workpiece without contact. In both cases, the required tool-path is specified with respect to the workpiece. A task is performed by mounting the tool on a robot, while a second robot grips the workpiece; the two robots are then coordinated to move simultaneously relative to one another so that the tool follows its prescribed trajectory at a constant speed relative to the workpiece, and provides sufficient contact force in the case of contact operations. The original tool-trajectory is thus resolved into a pair of conjugate trajectories, specified in task space relative to an inertial frame, describing the motion of the two robots. In cases where the two robots form a kinematically redundant system, the trajectory resolution process can yield an infinity of conjugate-trajectory pairs corresponding to a given original tool-trajectory, of which one pair may be chosen based on a specific choice of cost function. This article presents a technique whereby the robot's conjugate trajectories are parameterized using polynomial functions. A method is then developed for optimizing the robot's motion by selecting the optimal pair of conjugate trajectories. The proposed optimal trajectory-resolution technique is further enhanced by coupling it to a procedure for selecting the optimal relative placement of the two robots, resulting in the best achievable solution. This method of coordinating two robots yields lower cycle-time values than the corresponding single-robot operation, as well as reduces the need for complex jigs and fixtures. Numerical simulations are presented to illustrate the proposed technique. © 1994 John Wiley & Sons, Inc.