Tethered mobile robots are ideal for electrically noisy environments and for time-consuming tasks that require robust data communication and uninterrupted power delivery. However, tethers may become entangled in cluttered environments, leading to immobilisation and consequent mission failure. This work addresses real-time monitoring of tethers to detect tether entanglement, perform disentanglement through tether following and localise within line of sight. Experimental hardware is proposed to implement the tether monitoring techniques. Experiments are performed for single and dual mobile robots to search a target environment and entanglement detection is shown to be successful using quantitative metrics such as mean localization error.

In this paper, motion synchronization is investigated for human–robot collaboration such that the robot is able to “actively” follow its human partner. Force tracking is achieved with the proposed method under the impedance control framework, subject to uncertain human limb dynamics. Adaptive control is developed to deal with point-to-point movement, and learning control and neural networks control are developed to generate periodic and arbitrary continuous trajectories, respectively. Stability and tracking performance of the closed-loop system are discussed through rigorous analysis. The validity of the proposed method is verified through simulation and experiment studies.

In this paper a motion planner for nonholonomic stratified systems was proposed. Those systems may arise easily when reliable systems are designed to be robust against failures in difficult servicing environments. For a special class of the systems, a strong controllability condition was introduced, and a criterion to satisfy the condition was formulated and used to plan the motion of free-floating space manipulators. Modules of the planner were enumerated and their roles were emphasized. Some features of the planner were examined and discussed based on simulation results performed on two models of space manipulators.

We present two methods to find all the possible conformations of short six degree-of-freedom segments of biopolymers which satisfy end constraints in position and orientation. One of our methods is motivated by inverse kinematic solution techniques which have been developed for “general” 6R serial robotic manipulators. However, conventional robot kinematics methods are not directly applicable to the geometry of polymers, which can be treated as a degenerate case where all the “link lengths” are zero. Here, we propose a method which extends the elimination method of Kohli and Osvatic. This method can be applied directly to the geometry of biopolymers. We also propose a heuristic method based on a Lie-group-theoretic description. In this method, we utilize inverse iterations of the Jacobian matrix to obtain all conformations which satisfy end constraints. This can be easily implemented for both the general 6R manipulator and polymers. Although the extended elimination method is computationally faster than the Jacobian method, in cases where some of the joint angles are 180° (i.e., where the elimination method fails), we combine these two methods effectively to obtain the full set of inverse kinematic solutions. We demonstrate our approach with several numerical examples.

In this paper, we investigate dynamic walking as a convergence to the system's own limit cycles, not to artificially generated trajectories, which is one of the lessons in the concept of passive dynamic walking. For flexible walking, gait transitions can be performed by moving from one limit cycle to another one, and thus, the flexibility depends on the range in which limit cycles exist. To design a bipedal walker based on this approach, we explore period-1 passive limit cycles formed by natural dynamics and analyze them. We use a biped model with knees and point feet to perform numerical simulations by changing the center of mass locations of the legs. As a result, we obtain mass distributions for the maximum flexibility, which can be attained from very limited location sets. We discuss the effect of parameter variations on passive dynamic walking and how to improve robot design by analyzing walking performance. Finally, we present a practical application to a real bipedal walker, designed to exhibit more flexible walking based on this study.

In this paper, we propose and examine a force-resisting balance control strategy for a walking biped robot under the application of a sudden unknown, continuous force. We assume that the external force is acting on the pelvis of a walking biped robot and that the external force in the z-direction is negligible compared to the external forces in the x- and y-directions. The main control strategy involves moving the zero moment point (ZMP) of the walking robot to the center of the robot's sole resisting the externally applied force. This strategy is divided into three steps. The first step is to detect an abnormal situation in which an unknown continuous force is applied by examining the position of the ZMP. The second step is to move the ZMP of the robot to the center of the sole resisting the external force. The third step is to have the biped robot convert from single support phase (SSP) to double support phase (DSP) for an increased force-resisting capability. Computer simulations and experiments of the proposed methods are performed to benchmark the suggested control strategy.

This paper presents a novel configuration optimization method for multi degree-of-freedom modular reconfigurable robots (MRR) using a memetic algorithm (MA) that combines genetic algorithms (GAs) and a local search method. The proposed method generates multiple solutions to the inverse kinematics (IK) problem for any given spatial task and the MA chooses the most suitable configuration based on the search objectives. Since the dimension of each robotic link in this optimization is considered telescopic, the proposed method is able to find better solutions to the IK problem than GAs. The case study for a 3-DOF MRR shows that the MA finds solutions to the IK problem much faster than a GA with noticeably less reachability error. Additional case studies show that the proposed MA method can find multiple IK solutions in various scenarios and identify the fittest solution as a suboptimal configuration for the MRR.

This paper deals with the design of the control structure of a lower-limbs rehabilitation device in closed muscular chain called Sys-Reeduc. This control structure aims at providing a safe behavior to the user when performing rehabilitation exercises. It is based on two levels. The first level is concerned with the robust trajectory tracking of robotic device and has been the subject of previous studies. Nevertheless, it does not allow, by itself, the user to voluntarily drive the device. Therefore, a trajectory generator constituting the second level is presented in this paper to complete the whole control structure. This high-level control layer is described by a set of dedicated discrete state machines that provide the appropriate sequencing of elementary rehabilitation movements. These elementary movements are dynamically characterized so that clinician may choose the required trajectory parameters to adapt rehabilitation protocols and training to each individual. To realize a complete rehabilitation exercise, the sequence of elementary movements is triggered by thresholds relative to the measurement of the efforts applied by the user on the device. This allows the user to play an active role in its rehabilitation exercises and safely drive the machine at his/her own initiative. The design of the main exercises (isokinetic, isometric, and isotonic) used in the context of lower limbs rehabilitation is described, and simulation results illustrate the effectiveness of the proposed trajectory generator-based control approach.

Robots with micro- and nanoresolution of motion are becoming more practical and useful in many precision manufacturing processes and industries such as medical instruments and imaging tools. Apparently, the most important features of these devices are their precision and durability. As accuracy increases, more delicate tasks may be performed. Along this line, a spatial micromanipulator with three revolute–revolute–prismatic joints while equipped with nanometer motion resolution is considered here. At the end of the micromanipulator, a piezoresistive-based microcantilever operates as a force sensor to quantify the amount of the strain generated in the microcantilever and transduces it into a proper voltage for force sensing applications. In terms of the controller design, the value of the produced voltage can further be implemented as the feedback entering into the control loop and making the control unit to produce appropriate signals for manipulating the robot arm. A challenging and important problem is the need to control the applied boundary forces at the contact zone with external objects (specifically the biological samples). The ability to control the interaction force is of most interest today which has numerous applications in precision manufacturing and biomedical engineering. For this purpose, two types of controllers are presented here: a Lyapunov-based proportional-derivative (PD) controller and a robust adaptive (RA) controller. The performance and the stability of these two controllers are examined and discussed thoroughly in this paper so that it can be interpreted that the robust adaptive controller is robust under presence of uncertainties in force tracking control purposes.

In this paper we propose a dynamic image-based visual servoing (IBVS) control for a rotary wing unmanned aerial vehicle (UAV) which directly accounts for the vehicle's underactuated dynamic model. The motion control objective is to follow parallel lines and is motivated by power line inspection tasks where the UAV's relative position and orientation to the lines are controlled. The design is based on a virtual camera whose motion follows the onboard physical camera but which is constrained to point downwards independent of the vehicle's roll and pitch angles. A set of image features is proposed for the lines projected into the virtual camera frame. These features are chosen to simplify the interaction matrix which in turn leads to a simpler IBVS control design which is globally asymptotically stable. The proposed scheme is adaptive and therefore does not require depth estimation. Simulation results are presented to illustrate the performance of the proposed control and its robustness to calibration parameter error.

This paper presents an improved Rao-Blackwellized particle filtering framework with consideration of the particle swarm characteristics in FastSLAM, called Relational FastSLAM or R-FastSLAM. The R-FastSLAM seeks to cope with the inherent problems of FastSLAM, i.e., a particle depletion problem and an error accumulation problem in large environments. The R-FastSLAM uses the particle swarm characteristics in calculating the importance weight and maintaining a particle formation. We assign more accurate weights to particles by clustering them using the Expectation-Maximization (EM) algorithm according to an adaptive weight compensation scheme. In addition, particles constitute an adaptive triangular mesh formation to maintain multiple data association hypotheses without any resampling step. Its outstanding accomplishments are verified on simulations and a test using the Victoria Park dataset by comparing the standard FastSLAM 2.0 with the particle swarm optimization based FastSLAM.

This paper introduces a novel 6-DOF parallel manipulator, which is composed of two 3-RUS parallel manipulators that share a common three-dimensional moving platform. Semi-analytical form solutions are easily obtained to solve the forward displacement analysis of the robot using the non-planar geometry of the moving platform, whereas the velocity, acceleration, and singularity analyses are performed using screw theory. A case study is included to show the application of the kinematic model, which is verified with the aid of a commercially available software. Simple kinematic analysis and reduced singular regions are the main benefits of the proposed parallel manipulator.

This paper treats the systematic injury analysis of lower arm robot–human impacts. For this purpose, a passive mechanical lower arm (PMLA) was developed that mimics the human impact response and is suitable for systematic impact testing and prediction of mild contusions and lacerations. A mathematical model of the passive human lower arm is adopted to the control of the PMLA. Its biofidelity is verified by a number of comparative impact experiments with the PMLA and a human volunteer. The respective dynamic impact responses show very good consistency and support the fact that the developed device may serve as a human substitute in safety analysis for the described conditions. The collision tests were performed with two different robots: the DLR Lightweight Robot III (LWR-III) and the EPSON PS3L industrial robot. The data acquired in the PMLA impact experiments were used to encapsulate the results in a robot independent safety curve, taking into account robot's reflected inertia, velocity and impact geometry. Safety curves define the velocity boundaries on robot motions based on the instantaneous manipulator dynamics and possible human injury due to unforeseen impacts.

It is estimated that allergies afflict up to 40% of the world's population. A primary mediator for allergies is the aggregation of antigens and IgE antibodies bound to cell-surface receptors, FcεRI. Antibody/antigen aggregate formation causes stimulation of mast cells and basophils, initiating cellular degranulation and releasing immune mediators which produce an allergic or anaphylactic response. Understanding the shape and structure of these aggregates can provide critical insights into the allergic response. We have previously developed methods to geometrically model, simulate and analyze antibody aggregation inspired by rigid body robotic motion simulations. Our technique handles the large size and number of molecules involved in aggregation, providing an advantage over traditional simulations such as molecular dynamics (MD) and coarse-grained energetic models. In this paper, we study the impact of model resolution on simulations of geometric structures using both our previously developed Monte Carlo simulation and a novel application of rule-based modeling. These methods complement each other, the former providing explicit geometric detail and the latter providing a generic representation where multiple resolutions can be captured. Our exploration is focused on two antigens, a man-made antigen with three binding sites, DF3, and a common shrimp allergen (antigen), Pen a 1. We find that impact of resolution is minimal for DF3, a small globular antigen, but has a larger impact on Pen a 1, a rod-shaped molecule. The volume reduction caused by the loss in resolution allows more binding site accessibility, which can be quantified using a rule-based model with implicit geometric input. Clustering analysis of our simulation shows good correlation when compared with available experimental results. Moreover, collisions in all-atom reconstructions are negligible, at around 0.2% at 90% reduction.

Visual Homing is a navigation method based on comparing a stored image of a goal location to the current image to determine how to navigate to the goal location. It is theorized that insects such as ants and bees employ visual homing techniques to return to their nest or hive, and inspired by this, several researchers have developed elegant robot visual homing algorithms. Depth information, from visual scale, or other modality such as laser ranging, can improve the quality of homing. While insects are not well equipped for stereovision, stereovision is an effective robot sensor. We describe the challenges involved in using stereovision derived depth in visual homing and our proposed solutions. Our algorithm, Homing with Stereovision (HSV), utilizes a stereo camera mounted on a pan-tilt unit to build composite wide-field stereo images and estimate distance and orientation from the robot to the goal location. HSV is evaluated in a set of 200 indoor trials using two Pioneer 3-AT robots showing it effectively leverages stereo depth information when compared to a depth from scale approach.

Inverse kinematics solutions are the basis for position and orientation control of automated machines in their Cartesian workspace. This paper presents an efficient and robust inverse kinematics algorithm for a new circumferential drilling machine for aircraft fuselage assembly. After a brief introduction to the circumferential drilling machine and its forward kinematics, the paper discusses the nonlinear optimization method for solving inverse kinematics problems. The objective function is defined as a weighted combination of a position error function and an orientation error function. By representing orientation error as the geodesic distance between two points on a unit sphere, the paper proposes to define the orientation error function by using faithful geodesic distance functions, which are accurate approximations to the geodesic distance when it is small. For increased efficiency, robustness, and easy setting of initial values, the inverse kinematics problem is decomposed into two subproblems. The revolute joint coordinates are obtained by nonlinear optimization, and the prismatic joint coordinates are calculated with closed-form formulas. Numerical experiments show that the objective function defined with faithful geodesic distance functions is effective, and the proposed algorithm is efficient, robust, and accurate. The algorithm has been successfully integrated into the control system of the circumferential drilling machine. Preliminary drilling experiments show that the position accuracy of drilled holes is within ±0.5 mm, which is acceptable for the assembly of large aircrafts.

Autonomous docking is a focus of research in the field of self-assembly robots. Navigation is a significant stage in the process of autonomous docking between two robotic modules; it determines the efficiency of docking and even the success and failure of the docking task. In most cases, it is too difficult to simultaneously satisfy both linear and angular displacement constraints in a single dynamic numerical computation process. In the present paper, the navigation process is divided into two stages: first, the angular displacement constraint is satisfied, and then the linear displacement condition is fulfilled. In this way, the constraints are loosened and the difficulty of numerical computation is thereby effectively reduced. This two-stage docking navigation model is the main contribution of the present work. By taking the non-holonomic nature of the navigation behavior into consideration, both kinematic and dynamic analyses are performed, and the voltage data of the DC motors required by the two-stage docking navigation are obtained. Finally, docking navigation experiments are completed on a self-assembly modular robot named Sambot. It is verified that the present two-stage strategy is effective in controlling the docking navigation process.

In robotics, simulation has become an essential tool for research, education, and design purposes. Various software tools for mobile robot simulation have been developed and have reached different levels of maturity in recent years. This paper presents a general survey of mobile robot simulation tools and discusses qualitative and quantitative aspects of selection of four major simulators publicly available at no cost: Carmen, Player-Stage-Gazebo, Open Dynamics Engine, and Microsoft Robotics Developer Studio. The comparison of the simulators is aimed at establishing the range of applications for which these are best suited as well as their accuracy for certain simulation tasks. The simulators chosen for detailed comparison were selected considering their level of maturity, modularity, and popularity among engineers and researchers. The qualitative comparison included a discussion of relevant features. The quantitative analysis entailed the development of a detailed dynamical model of a mobile robot on a road with varying slope. This model was used as benchmark to compare the accuracy of each simulator. The validity of the simulated results was also contrasted against measurements obtained from experiments with a real robot. This research and analysis should be very valuable to educators, engineers, and researchers who are always seeking adequate tools for simulating autonomous mobile robots.

This paper discusses about a proposed solution to the obstacle avoidance problem in multi-robot systems when applied to active target tracking. It is explained how a nonlinear model predictive formation control (NMPFC) previously proposed solves this problem of fixed and moving obstacle avoidance. First, an approach is presented which uses potential functions as terms of the NMPFC. These terms penalize the proximity with mates and obstacles. A strategy to avoid singularity problems with the potential functions using a modified A* path planning algorithm was then introduced. Results with simulations and experiments with real robots are presented and discussed demonstrating the efficiency of the proposed approach.

Joints of space manipulators are usually simplified as torsional springs in modeling motion equations, and the nonlinear behaviors of the reducer in the joints are generally neglected. In this study, a dynamic model of a space manipulator that considers the joints that are transmitted through a typical 2K-H planetary gear reducer is developed using the Lagrangian method. The backlash clearances, gear tooth profile error, and time-variant meshing stiffness are integrated into the process. The simulation results show that the backlash clearances lead to the accumulation of positioning errors in the space manipulator when the joints rotate back and forth. The tooth profile error is the main cause of severe acceleration fluctuations and meshing force impacts. These fluctuations influence torque instability, which may accelerate gear system failure.