This work presents an approach to motion planning for robotic manipulators that aims at improving path quality in terms of safety. Safety is explicitly assessed using the quantity called danger field. The measure of safety can easily be embedded into a heuristic function that guides the exploration of the free configuration space. As a result, the resulting path is likely to have substantially higher safety margin when compared to one obtained by regular planning algorithms. To this end, four planning algorithms have been proposed. The first planner is based on volume trees comprised of bubbles of free configuration space, while the remaining ones represent modifications of classical sampling-based algorithms. Several numerical case studies are carried out to validate and compare the performance of the presented algorithms with respect to classical planners. The results indicate significantly lower danger metric for paths obtained by safety-oriented planners even with some decrease in running time.

Central Pattern Generators (CPGs) can generate robust, smooth and coordinated oscillatory signals for locomotion control of robots with multiple degrees of freedom, but the tuning of CPG parameters for a desired locomotor pattern constitutes a tremendously difficult task. This paper addresses this problem for the generation of fish-like swimming gaits with an adaptive CPG network on a multi-joint robotic fish. Our approach converts the related CPG parameters into dynamical systems that evolve as part of the CPG network dynamics. To reproduce the bodily motion of swimming fish, we use the joint angles calculated with the trajectory approximation method as teaching signals for the CPG network, which are modeled as a chain of coupled Hopf oscillators. A novel coupling scheme is proposed to eliminate the influence of afferent signals on the amplitude of the oscillator. The learning rules of intrinsic frequency, coupling weight and amplitude are formulated with phase space representation of the oscillators. The frequency, amplitudes and phase relations of the teaching signals can be encoded by the CPG network with adaptation mechanisms. Since the Hopf oscillator exhibits limit cycle behavior, the learned locomotor pattern is stable against perturbations. Moreover, due to nonlinear characteristics of the CPG model, modification of the target travelling body wave can be carried out in a smooth way. Numerical experiments are conducted to validate the effectiveness of the proposed learning rules.

This paper presents a modified passive dynamic walking model with hip friction. We add Coulomb friction to the hip joint of a two-dimensional straight-legged passive dynamic walker. The walking map is divided into two parts – the swing phase and the impact phase. Coulomb friction and impact make the model's dynamic equations nonlinear and non-smooth, and a numerical algorithm is given to deal with this model. We study the effects of hip friction on gait and obtain basins of attraction of different coefficients of friction.

Performance evaluation of a parallel robot is a multicriteria problem. By taking Delta robot as an object of study, this paper presents the kinematic performance evaluation of a three translational degrees-of-freedom parallel robot from the viewpoint of singularity, isotropy, and velocity transmission. It is shown that the determinant of a Jacobian matrix cannot measure the distance from the singular configuration due to the existing inverse kinematic singularity of a Delta robot. The determinants of inverse and direct kinematic Jacobian matrices are adopted for the measurement of distance from the singular configuration based on the theory of numerical linear dependence. The denominator of the Jacobian matrix will be lost in the computation of the condition number when the end-effector is on the centerline of the workspace, so the Delta robot may also be nearly at a singular configuration when the condition number of the Jacobian matrix is equal to 1. The velocity transmission index whose physical meaning is the maximum input angular velocity when the end-effector translates in the unit velocity is presented. The evaluation of singularity, isotropy, and velocity transmission of a Delta robot is investigated by simulation. The velocity transmission index can also be used for the velocity transmission evaluation of a parallel robot with pure rotational degrees-of-freedom based on the principle of similarity. The physical meaning is modified to be the maximum input velocity when the end-effector rotates in the unit angular velocity.

This paper proposes a repetitive control type optimal gait generation framework by executing learning control and parameter tuning. We propose a learning optimal control method of Hamiltonian systems unifying iterative learning control (ILC) and iterative feedback tuning (IFT). It allows one to simultaneously obtain an optimal feedforward input and tuning parameter for a plant system, which minimizes a given cost function. In the proposed method, a virtual constraint by a potential energy prevents a biped robot from falling. The strength of the constraint is automatically mitigated by the IFT part of the proposed method, according to the progress of trajectory learning by the ILC part.

This paper proposes a precise outdoor localization algorithm with the integration of Global Positioning System (GPS) and Inertial Navigation System (INS). To achieve precise outdoor localization, two schemes are recently proposed, which consist of de-noising the INS signals and fusing the GPS and INS data. To reduce the noise from the internal INS sensors, the discrete wavelet transform and variable threshold method are utilized, and to fuse the GPS and INS data while filtering out the noise caused by the acceleration, deceleration, and unexpected slips, the Unscented Particle Filter (UPF) is adopted. Conventional de-noising methods mainly employ a combination of low-pass and high-pass filters, which results in signal distortion. This newly proposed system also utilizes the vibration information of the actuator according to the fluctuations of the velocity to minimize the signal distortion. The UPF resolves the nonlinearities of the actuator and non-normal distributions of the noise more effectively than the conventional particle filter (PF) or Extended Kalman Filter–PF. The superiority of the proposed algorithm was verified through experiments, and the results are reported.

The aim of this paper is to describe a general methodology for enlarging the workspace within which a parallel manipulator can move in a controllable way. The basis for obtaining this consists in superimposing all the singularity-free regions associated with the various different robot working modes. These can be connected because such transitions do not imply a loss of control of the manipulator. This enlarged operational workspace is associated with a certain assembly mode. In addition, the strategy to be used for path planning in this kind of workspace is presented.

Walking locomotion involves complex movement of total center of mass. Not only the lower body behavior but also the upper body behavior affects the walking characteristics. Therefore, in this paper we derive the principle of parametrically excited inverted double pendulum to consider both lower body and upper body dynamics. We propose one approach to utilize the upper body behavior of the robot for energy efficient bipedal locomotion. In addition, we analyze the property of parametrically excited inverted double pendulum.

This paper delivers a proof of a statement due to Ge and Lee. Specifically, these authors stated, without proof, that the entries of the inertia matrix may be completely parameterized by stacking elements of a regressor superset. This superset has the advantage of avoiding to derive the complete dynamics of a robot manipulator. On the basis of both mechanics and combinatorial arguments, we deliver a formal proof. In addition, we improve the estimations by sorting joint variables and partitioning the inertia matrix that results into the reduction of the regressor superset. The number of nonlinear functions in the regressor is also quantified. A 2 degrees of freedom revolute robot is presented to illustrate these ideas.

Similar to the traditional adaptive strategies for robot manipulators, the regressor-free adaptive controller design also requires applying Slotine and Li's modification to avoid the feedback of joint accelerations. In this paper, a simple method is proposed to construct a regressor-free adaptive controller for robot manipulators without Slotine and Li's modification. In the new design, the joint acceleration vector is lumped into an unknown time-varying function and the function approximation technique is utilized to cover its effect; therefore, its implementation is free from joint acceleration feedback. The closed-loop stability and boundedness of internal signals are justified by the Lyapunov-like technique. Both simulation and experimental results for a two-link robot are presented to show the effectiveness of the proposed design.

Fast and robust tracking against unknown disturbances is required in many modern complex robotic structures and applications, for which knowledge of the full exact nonlinear system is unreasonable to assume. This paper proposes a regressor-free nonlinear controller of low complexity which ensures prescribed performance position error tracking subject to unknown endogenous and exogenous bounded dynamics assuming that joint position and velocity measurements are available. It is theoretically shown and demonstrated by a simulation study that the proposed controller can guarantee tracking of the desired joint position trajectory with a priori determined accuracy, overshoot and speed of response. Preliminary experimental results to a simplified system are promising for validating the controller to more complex structures.

The use of extra sensors in parallel robots can provide an increase in control performance, making it possible to fully exploit the potential of these mechanisms. In this paper, a comprehensive redundant dynamic modelling procedure for the six-degree-of-freedom Gough platform is presented. The proposed methodology makes it possible to define the model in terms of all sensorized joint variables in order to implement redundant information-based control, and an example, the Extended Computed Torque Control (Extended CTC) approach, is developed. This, applied to parallel robots, ensures better dynamic performance than the traditional CTC approach. In order to validate dynamic modelling, a two-step procedure is used in this paper. First, the redundant dynamic model is validated by comparing its dynamic performance with the previous research in the field. Second, an exhaustive study is carried out that demonstrates the advantages of the redundant dynamic model when used in the Extended CTC approach.

This paper introduces an optimal interval type-2 fuzzy proportional–integral–derivative (PID) controller to achieve the best trajectory tracking for nonholonomic wheeled mobile robots (WMRs). In the core of the proposed method, a novel population-based optimization algorithm, called teaching–learning-based optimization (TLBO), is employed for evolving the parameters of the controller as well as the parameters of the input and output membership functions. Two PID controllers are designed for each of two wheels separately whereas each controller has two inputs and one output that are logically connected by nine rules. The controller can handle the problem of integrated kinematic and dynamic tracking in the presence of uncertainties. Simulation results demonstrate the superiority of the proposed control scheme.

In this paper, a novel optimal torque distribution method for a redundantly actuated parallel robot is proposed. Geometric analysis based on screw theory is performed to calculate the stiffness matrix of a redundantly actuated 3-RRR parallel robot. The analysis is performed based on statics focusing on low-speed motions. The stiffness matrix consisting of passive and active stiffness is also derived by the differentiation of Jacobian matrix. Comparing two matrices, we found that null-space vector is related to link geometry. The optimal distribution torque is determined by adapting mean value of minimum and maximum angles as direction angles of null-space vector. The resulting algorithm is validated by comparing the new method with the minimum-norm method and the weighted pseudo-inverse method for two different paths and force conditions. The proposed torque distribution algorithm shows the characteristics of minimizing the maximum torque.

This paper presents a multi-agent behavior to cooperatively rescue a faulty robot using a sound signal. In a robot team, the faulty robot should be immediately recalled since it may seriously obstruct other robots, or collected matters in the faulty robot may be lost. For the rescue mission, we first developed a sound localization method, which estimates the sound source from a faulty robot by using multiple microphone sensors. Next, since a single robot cannot recall the faulty robot, the robots organized a heterogeneous rescue team by themselves with pusher, puller, and supervisor. This self-organized team succeeded in moving the faulty robot to a safe zone without help from any global positioning systems. Finally, our results demonstrate that a faulty robot among multi-agent robots can be immediately rescued with the cooperation of its neighboring robots and interactive communication between the faulty robot and the rescue robots. Experiments are presented to test the validity and practicality of the proposed approach.

This study addresses the kinematics of a six-degrees-of-freedom parallel manipulator whose moving platform is a regular triangular prism. The moving and fixed platforms are connected to each other by means of two identical parallel manipulators. Simple forward kinematics and reduced singular regions are the main benefits offered by the proposed parallel manipulator. The Input–Output equations of velocity and acceleration are systematically obtained by resorting to reciprocal-screw theory. A case study, which is verified with the aid of commercially available software, is included with the purpose to exemplify the application of the method of kinematic analysis.

This paper presents applications of group theory tools to simplify the analysis of kinematic chains associated with mechanisms and parallel manipulators. For the purpose of this analysis, a kinematic chain is described by its properties, i.e. degrees-of-control, connectivity and redundancy matrices. In number synthesis, kinematic chains are represented by graphs, and thus the symmetry of a kinematic chain is the same as the symmetry of its graph. We present a formal definition of symmetry in kinematic chains based on the automorphism group of its associated graph. The symmetry group of the graph is associated with the graph symmetry. By using the group structure induced by the symmetry of the kinematic chain, we prove that degrees-of-control, connectivity and redundancy are invariants by the action of the automorphism group of the graph. Consequently, it is shown that it is possible to reduce the size of these matrices and thus reduce the complexity of the kinematic analysis of mechanisms and parallel manipulators in early stages of mechanisms design.

It has been shown before that the Rapidly Exploring Random Tree (RRT) algorithm is probabilistically and resolution complete; and that the probability of finding a particular solution path can be related to the number of nodes. However, little analysis exists on the rate at which the tree covers the configuration space. In this paper, we present a stochastic difference equation which models how the tree covers the configuration space as a function of the number of nodes in the tree. Using two simplifying assumptions, appropriate for holonomic, kinematic systems in expansive configuration spaces, we derive closed-form solutions for the expected value and variance of configuration space coverage, which only depend on two easily computable parameters. Using a grid-based coverage measurement, we present experimental evidence supporting this model across a range of dimensions, obstacle densities, and parameter choices. Collecting data from 1000 RRTs, we provide evidence that configuration space coverage concentrates tightly around the expected coverage predicted by the model; and the results of the Chi-squared test suggest that the distribution of coverage across these runs is highly Gaussian. Together these results enable one to predict the expected coverage, along with a confidence interval, after a certain number of nodes have been added to the tree. We also applied the model to an example with extremely narrow passages and to a system with non-holonomic kinematics. The expected value prediction is still qualitatively accurate; but the rate constant is reduced and the variance is higher. Overall, in addition to its theoretical value, the model may find future application as an online measure of search-progress and problem difficulty, useful for adaptive variants of the basic RRT algorithm.

In this paper, design, dynamic, and control of the motors of a spatial cable robot are presented considering flexibility of the joints. End-effector control in order to control all six spatial degrees of freedom (DOFs) of the system and motor control in order to control the joints flexibility are proposed here. Corresponding programing of its operation is done by formulating the kinematics and dynamics and also control of the robot. Considering the existence of gearboxes, flexibility of the joints is modeled in the feed-forward term of its controller to achieve better accuracy. A two sequential closed-loop strategy consisting of proportional derivative (PD) for linear actuators in joint space and computed torque method for nonlinear end-effector in Cartesian space is presented for further accuracy. Flexibility is estimated using modeling and simulation by MATLAB and SimDesigner. A prototype has been built and experimental tests have been done to verify the efficiency of the proposed modeling and controller as well as the effect of flexibility of the joints. The ICaSbot (IUST Cable-Suspended robot) is an under-constrained six-DOF parallel robot actuated by the aid of six suspended cables. An experimental test is conducted for the manufactured flexible joint cable robot of ICaSbot and the outputs of sensors are compared with simulation. The efficiency of the proposed schemes is demonstrated.

This paper summarizes the recent standardization activities in the field of robotics by ISO (International Organization for Standardization), IEC (International Electrotechnical Commission), OMG (Object Management Group), and other organizations. While the standards in industrial robots have been mainly developed by ISO, the standards on the emerging service robots are initiated by many organizations. One of the goals of this paper is to coordinate the efforts among these groups so that more effective standardization activity can be executed. Standardization in the emerging service robots will eventually promote the proliferation of service robot markets in the near future.