When compared to serial manipulators, parallel manipulators have small workspaces mainly due to their closed-loop structure. As opposed to type I singularities (or inverse kinematic singularities) that are generally encountered at the workspace boundaries, type II singularities characteristically arise within the workspace, and around them, the inverse dynamic solution becomes unbounded. Hence, a desired trajectory passing through a type II singular position cannot be achieved by the manipulator, and its useful workspace becomes further and substantially reduced. It has been previously shown in the literature that if the trajectory is replanned in such a way that the dynamic equations of motion of the manipulator are consistent at a type II singularity, i.e. if the trajectory is consistent, then the manipulator passes through this singular configuration in a controllable manner, while the inverse dynamic solution remains finite. An inconsistent trajectory, on the other hand, is stated in the literature to be unrealizable. However, although seems a promising technique, trajectory replanning itself is also a deviation from the originally desired trajectory, and there might be cases in applications where, due to some task-specific reasons, the desired trajectory, although inconsistent, is not allowed to be replanned to satisfy the consistency conditions. In this paper, a method of singularity robust balancing is proposed for parallel manipulators passing through type II singular configurations while following inconsistent trajectories. By this means, an originally unrealizable inconsistent trajectory passing through a type II singularity can be followed without any deviation, while the required actuator forces remain bounded after the manipulator is balanced according to the design methodology presented in this study. The effectiveness of the introduced method is shown through numerical simulations considering a planar 3-DOF 2-PRR parallel manipulator under different balancing scenarios.

This paper considers the problem of merging grid maps that have different resolutions. Because the goal of map merging is to find the optimal transformation between two partially overlapping grid maps, it can be viewed as a special image registration issue. To address this special issue, the solution considers the non-common areas and designs an objective function based on the trimmed mean-square error (MSE). The trimmed and scaling iterative closest point (TsICP) algorithm is then proposed to solve this well-designed objective function. As the TsICP algorithm can be proven to be locally convergent in theory, a good initial transformation should be provided. Accordingly, scale-invariant feature transform (SIFT) features are extracted for the maps to be potentially merged, and the random sample consensus (RANSAC) algorithm is employed to find the geometrically consistent feature matches that are used to estimate the initial transformation for the TsICP algorithm. In addition, this paper presents the rules for the fusion of the grid maps based on the estimated transformation. Experimental results carried out with publicly available datasets illustrate the superior performance of this approach at merging grid maps with respect to robustness and accuracy.

This paper develops a new adaptive actuator failure compensation algorithm for the control of a cooperative robotic system subject to uncertain actuator failures. The benchmark robotic system has two manipulators to balance a rigid platform, and the right-side manipulator contains one actuator and the left-side manipulator has two actuators (one of which may fail during system operation, but it is uncertain how much, when and which actuator may fail). The developed adaptive actuator failure compensation scheme, based on adaptive integration of three individual failure compensators and direct adaptation of controller parameters, is capable of ensuring desired closed-loop stability and asymptotic output tracking, despite the failure uncertainties. Such an adaptive actuator failure compensation method is extended to control of a general cooperative robotic system. Simulation results are shown to verify the desired adaptive failure compensation control performance.

The cyclic coordinate descent (CCD) method is a popular loop closure method in protein structure modeling. It is a robotics algorithm originally developed for inverse kinematic applications. We demonstrate an effective method of building the backbone of protein structure models using the principle of CCD and a guiding trace. For medium-resolution 3-dimensional (3D) images derived using cryo-electron microscopy (cryo-EM), it is possible to obtain guiding traces of secondary structures and their skeleton connections. Our new method, constrained cyclic coordinate descent (CCCD), builds α-helices, β-strands, and loops quickly and fairly accurately along predefined traces. We show that it is possible to build the entire backbone of a protein fairly accurately when the guiding traces are accurate. In a test of 10 proteins, the models constructed using CCCD show an average of 3.91 Å of backbone root mean square deviation (RMSD). When the CCCD method is incorporated in a simulated annealing framework to sample possible shift, translation, and rotation freedom, the models built with the true topology were ranked high on the list, with an average backbone RMSD100 of 3.76 Å. CCCD is an effective method for modeling atomic structures after secondary structure traces and skeletons are extracted from 3D cryo-EM images.

Based on the results of the study of convex object motion1 (J. Hopcroft and G. Wilfong, “Motion of objects in contact,” Int. J. Robot. Res., 4(4), 32–46 (1986)), this paper addresses the problem of exact collision detection of a pair of scaled convex polyhedra in relative motion, and determines the contact conditions of tangential contact features, arbitrary relative motion involving translation and rotation, and uniform scaling of the objects about a fixed point. We propose a new concept of the decision curve based on analytical contact equations that characterize a continuum of scaling factors (or a single scaling factor), which ensures that a pair of objects undergoing a scaling transformation will maintain the same tangential contact feature pair (or make instantaneous tangential contact feature transitions). We propose a reliable simulation-based approach to construct the decision curve by hybridizing analytical contact equations and conventional collision detection method, called the Fast Collision Detection Method (FCDM). This method can determine whether two scaled objects will make contact at specific tangential contact features (vertices, edges, or faces) under particular uniform scaling factors and after distinctive relative motion with better accuracy and less computational time than the existing collision detection methods. Finally, we demonstrate our approach for solving motion design in simple assembly/disassembly problems.

This paper presents a new solution approach for managing the motion of a fleet of autonomous vehicles (AVs) in indoor factory environments. AVs are requested to serve a number of workstations (WS) (following a specified desired production plan for materials requirements) while taking into account the safe movement (collisions avoidance) in the shop floor as well as time duration and energy resources. The proposed approach is based on the Bump-Surface concept to represent the 2D environment through a single mathematical entity. The solution of the combined problem of path planning and task scheduling is searched on a higher-dimension B-surface (in our case 3D) in such a way that its inverse image into the robot environment satisfies the given objectives and constraints. Then, a modified Genetic Algorithm (GA) is used to search for a near-optimum solution. The objective of the fleet coordination consists of determining the best feasible paths for the AVs so that all the WS are served at the lowest possible cost. The efficiency of the developed method is investigated and discussed through characteristic simulated experiments concerning a variety of operating environments.

The current paper presents a path planning method based on probability maps and uses a new genetic algorithm for a group of UAVs. The probability map consists of cells that display the probability which the UAV will not encounter a hostile threat. The probability map is defined by three events. The obstacles are modeled in the probability map, as well. The cost function is defined such that all cells are surveyed in the path track. The simple formula based on the unique vector is presented to find this cell position. Generally, the cost function is formed by two parts; one part for optimizing the path of each UAV and the other for preventing UAVs from collision. The first part is a combination of safety and length of path and the second part is formed by an exponential function. Then, the optimal paths of each UAV are obtained by the genetic algorithm in a parallel form. According to the dimensions of path planning, genetic encoding has two or three indices. A new genetic operator is introduced to select an appropriate pair of chromosome for crossover operation. The effectiveness of the method is shown by several simulations.

Previous research has revealed that foot rotation of the supporting foot in a single support phase could increase walking speed. This paper presents a method for force-controlled bipeds to realize foot rotation by breaking the kinematic contact constraint between the supporting foot and the ground. An inverse dynamics controller is proposed to make the biped model controllable even when the constraint is broken. In addition, a linear inverted pendulum model is extended to make its ZMP adjustable so that the ZMP can be predefined as required. When the planned ZMP is in the toe, the kinematic contact constraint will be broken and foot rotation can be achieved. A walking simulation demonstrates the effectiveness of the proposed method.

A novel hybrid robotic arm with three fingers is proposed for assembly, operation and rescue. It is composed of an upper 3RPS-type parallel manipulator, a lower 3SPR-type parallel manipulator and three fingers. Its dynamics are studied and analyzed systematically. First, the kinematics formulae of the moving links at their mass centers in the hybrid robotic arm with three fingers are established. Second, the formulae for solving the inertial wrench of the moving links at their mass centers are derived. Third, a dynamics formula is established for solving the dynamic active/constrained forces of the hybrid robotic arm and the fingers. Finally, an analytic example is given for solving the kinematics and dynamics of the hybrid robotic arm with three fingers and the analytic solved solutions are verified by a simulation mechanism.

This paper proposes a new dynamic planning navigation strategy for use with mobile terrestrial robots. The strategy was applied to situations in which the environment and obstacles were unknown. After each displacement event, the robot replanned its route using a control algorithm that minimized the distance to the target and maximized the distance between the obstacles. Using a spatial localization sensor and a set of distance sensors, the proposed navigation strategy was able to dynamically plan optimum routes that were free of collisions. Simulations performed using different types of environment demonstrated that the technique offers a high degree of flexibility and robustness, and validated its potential use in real applications involving mobile terrestrial robots.

This paper provides a mathematical approach to study the duration of collision-free motion of multiple unmanned autonomous vehicles (UXVs) operating in confined areas. A simple geometric model of the UXVs is first proposed, and the dynamics of the model is shown. The expected time of first collision is then formulated using the concept of mean free path from molecular dynamics. Monte-Carlo simulation is performed to verify the theory developed. The expected time of first collision is a function of the number of UXVs, the UXV speed and the sensor field of view (FOV) for a given operational area and vehicle size. Furthermore, the critical number of UXVs, above which collision can be deemed to occur instantly, is obtained.

This paper investigates the trajectory tracking of a Stewart platform, which is a typical multi-input multi-output nonlinear system, with unmodeled dynamics, parameter uncertainties, friction, and unpredictable actuator faults. An adaptive vector sliding mode fault-tolerant control law is derived to ensure the system is insensitive to uncertainties and drive the state variable errors of the closed-loop system to converge to the origin. Moreover, novel adaptive laws are proposed to update the upper boundary of uncertainty according to the actual system state, which greatly reduces the chattering of sliding mode control. Furthermore, velocity signals are estimated by introducing a simple nonlinear observer, resulting in the proposed controller requiring position measurements only. Finally, numerical simulations illustrate the effectiveness of the proposed control scheme.

Detection of targets distributed randomly in space is a task common to both robotic and biological systems. Lévy search has previously been used to characterize T cell search in the immune system. We use a robot swarm to evaluate the effectiveness of a Lévy search strategy and map the relationship between search parameters and target configurations. We show that the fractal dimension of the Lévy search which optimizes search efficiency depends strongly on the distribution of targets but only weakly on the number of agents involved in search. Lévy search can therefore be tuned to the target configuration while also being scalable. Implementing search behaviors observed in T cells in a robot swarm provides an effective, adaptable, and scalable swarm robotic search strategy. Additionally, the adaptability and scalability of Lévy search may explain why Lévy-like movement has been observed in T cells in multiple immunological contexts.

A novel micro in-pipe robot using an internally threaded profile for propulsion is proposed in this paper, and the dynamic model of the robot in the turbulent liquid pipeline is established, and the computational fluid dynamics method is used to solve the influence of environmental parameters and operating parameters on the robotic performance. By the orthogonal experimental optimization method, the optimal inner spiral structural parametrical combination is obtained. According to the working principle of the inner spiral robot, an inner spiral driving device is designed and fabricated, and the running experiment in the pipeline full of 201 methyl silicone oil verifies the feasibility of the proposed robot. Adopting the pulsating blood flow function as the inlet condition, in a pulsating period, the robotic performance is numerically analyzed.

Real-time responses for multiple robots motion planning demand heuristic algorithms. This paper presents a method to evaluate the efficiency of these algorithms in order to compute coordinated trajectories for multiple car-like robots on a segment of a highway. The idea is to compare the results of these algorithms with the optimal result obtained by a proposed mixed-integer linear programming (MILP) optimization model. The MILP model considers the main elements of a traffic system, such as topography of lanes, traffic rules and individual capacity of acceleration. Moreover, new indexes for microscopic traffic assessment are proposed a well. Several tests have been carried out to validate both the MILP model and the algorithm used.

This paper is about the precision performance evaluation of an XY-theta platform, with a patented kinematic design. Indeed, this platform is held by a serial redundant robot arm actuated with four revolute joints. The platform offers a wide 300 × 300 mm workspace, whereas the whole mechanism is extremely compact. Any workpiece on the platform can be positioned under a vertical axis to be grasped or manufactured in a two-step approach: in a coarse positioning mode, the four revolute joints are controlled to position and orientate the workpiece with a position error of less than 10 μm; in a fine positioning mode, two revolute joints are mechanically blocked while two others are controlled to reduce the final error to below 2 μm. The mechanism design and the choice of the blocked and moving joints are optimized to enhance the positioning performances in the two-step positioning method. In this paper, the platform positioning repeatability and its spatial resolution are characterized with the help of a camera. The advantage of this method is that it avoids any mechanical contact and can be implemented easily. Then, these results are compared to our previous precision performance evaluation obtained with the stationary cube method. Finally, the positioning repeatability is estimated at 6.5 μm in the coarse positioning mode and 1.4 μm in the fine positioning mode. Between the coarse and fine mode, the repeatability is thus improved by the factor of four, as predicted by the theory.

Localization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of Range-Only (RO) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques to solve both problems. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approaches with RO sensors is quite incomplete. This paper presents a comparison between filtering algorithms, such as EKF and FastSLAM, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained in indoor environments using WiFi sensors. The results demonstrate the feasibility of the smoothing approach using WiFi sensors in an indoor environment.

This paper formulates a new scalable algorithm for motion planning and control of multiple point-mass robots. These autonomous robots are designated to move safely to their goals in a priori known workspace cluttered with fixed and moving obstacles of arbitrary positions and sizes. The control laws proposed for obstacle and collision avoidance and target convergence ensure that the equilibrium point of the given system is asymptotically stable. Computer simulations with the proposed technique and applications to a team of two planar (RP) manipulators working together in a common workspace are presented. Also, the robustness of the system in the presence of noise is verified through simulations.

Force control is important in robotics research for safe operation in the interaction between a manipulator and a human operator. The elasticity center is a very important characteristic for controlling the force of a manipulator, because a force acting at the elasticity center results in a pure displacement of the end-effector in the same direction as the force. Similarly, a torque acting at the elasticity center results in a pure rotation of the end-effector in the same direction as the torque. A stiffness synthesis strategy is proposed for a desired elasticity center for three-degree-of-freedom (DOF) planar parallel mechanisms (PPM) consisting of three revolute-prismatic-revolute (3RPR) links. Based on stiffness analysis, the elasticity center is derived to have a diagonal stiffness matrix in an arbitrary configuration. The stiffness synthesis is defined to determine the configuration when the elasticity center and the diagonal matrix are given. The seven nonlinear system equations are solved based on one reference input. The existence and the solvability of the nonlinear system equations were analyzed using reduced Gröbner bases. A numerical example is presented to validate the method.

This paper presents a new numerical approach using a Genetic algorithm (GA) to search for the singularity-free cylindrical space of a 4-RRR planar redundant parallel manipulator and investigates the effects of the joint position (namely the length ratios of two links) of each leg on the singularity-free cylindrical space. A previous method investigated the maximal singularity-free zone in a 3-dimensional (3-D) space within a given workspace. The method in this paper is improved by optimizing the maximal singularity-free zone in a 2-dimensional (2-D) plane while considering the whole workspace. This improvement can be helpful for reducing the searching time and for finding a larger singularity-free zone. Furthermore, the effect of the joint position of each leg on the maximal singularity-free zone is studied in this paper, which reveals a larger singularity-free zone than before. This result shows that changing the joint positions of one or two legs may be more practical than changing the joint positions of more legs. The approach in this paper can be used to analyze the maximal singularity-free zone of any other three-degree-of-freedom (3-DOF) planar parallel mechanisms and will be useful for the optimal design of redundant parallel manipulators.