Working with mobile robots, prior to execute the local planning stage, they must know the environment where they are moving. For that reason the perception and mapping stages must be performed previously. This paper presents a survey in the state of the art in detection and tracking of moving obstacles (DATMO). The aim of what follows is to provide an overview of the most remarkable methods at each field specially in indoor environments where dynamic obstacles can be potentially more dangerous and unpredictable. We are going to show related DATMO methods organized in three approaches: model-free, model-based and grid-based. In addition, a comparison between them and conclusions will be presented.

Repetitive motion planning and control (RMPC) is a significant issue in the research of redundant robot manipulators. Moreover, noise from rounding error, truncation error, and robot uncertainty is an important factor that greatly affects RMPC schemes. In this study, the RMPC of redundant robot manipulators in a noisy environment is investigated. By incorporating the proportional and integral information of the desired path, a new RMPC scheme with pseudoinverse-type (P-type) formulation is proposed. Such a P-type RMPC scheme possesses the suppression of constant and bounded time-varying noises. Comparative simulation results based on a five-link robot manipulator and a PUMA560 robot manipulator are presented to further validate the effectiveness and superiority of the proposed P-type RMPC scheme over the previous one.

Visual homing is a local navigation technique used to direct a robot to a previously seen location by comparing the image of the original location with the current visual image. Prior work has shown that exploiting depth cues such as image scale or stereo-depth in homing leads to improved homing performance. While it is not unusual to use a panoramic field of view (FOV) camera in visual homing, it is unusual to have a panoramic FOV stereo-camera. So, while the availability of stereo-depth information may improve performance, the concomitant-restricted FOV may be a detriment to performance, unless specialized stereo hardware is used. In this paper, we present an investigation of the effect on homing performance of varying the FOV widths in a stereo-vision-based visual homing algorithm using a common stereo-camera. We have collected six stereo-vision homing databases – three indoor and three outdoor. Based on over 350,000 homing trials, we show that while a larger FOV yields performance improvements for larger homing offset angles, the relative improvement falls off with increasing FOVs, and in fact decreases for the widest FOV tested. We conduct additional experiments to identify the cause of this fall-off in performance, which we term the ‘blinder’ effect, and which we predict should affect other correspondence-based visual homing algorithms.

This study presents a new design of a wearable orthosis of elbow joint with a bimuscular pneumatic servo-drive (PSD) with control based on the recording of bioelectric signals (BESs). The authors analyzed the impact of the induced brain activity and the muscular tension within the head of the participant on the BESs that can be used to control the PSD of the elbow joint orthosis. To control the elbow joint orthosis, a distributed control system (DCS) was developed, which contains two control layers: a master layer connected to the device for recording the BES and a direct layer contained in a wireless manner with the controller of the PSD. A kinematic-dynamic model of the elbow joint orthosis, patterned after the biological model of human biceps–triceps, was used in the programming of the PSD controller. A biomimetic dynamic model of the pneumatic muscle actuator (PMA) was used, in which the contraction force results from the adopted exponential static model of the pneumatic muscle (PM). The use of direct visual feedback (DVF) makes it possible for the participant to focus on the movement of the orthosis taking into account the motoric functions of the elbow.

Deformable linear objects (DLOs) have a wide variety of applications in a range of fields. Their key characteristic is that they extend much further in one of their dimensions than in the other two. Accurate motion planning is particularly important in the case of DLOs used in robotics applications. In this paper, a new strategy for planning the motions of DLOs under multiple constraints is proposed. The DLO was modeled as Cosserat elastic rods so that the deformation is simulated accurately and efficiently. The control of the motion of the DLO was enhanced by supplementing one gripper installed at each end with additional supports. This allows DLOs to undergo complex deformations, and thus avoid collisions during motion. The appropriate number of supports and their positions were determined, and then a rapidly exploring random tree algorithm was used to search for the best path to guide the DLO toward its target destination. The motion of the simulated DLO is described as it is controlled using two grippers and specific numbers of supports. To prove that the proposed DLO motion planning strategy can successfully guide relatively long DLOs through complex environments without colliding with obstacles, a case study of the strategy was conducted when guiding a DLO through a puzzle.

In this paper, we present and implement a novel approach for position-based visual servoing. The challenge of controlling the mobile robot while simultaneously estimating the camera to mobile robot transformation is solved. This is achieved using gradient descent (GD)-based estimation and the sliding-mode approach. The GD approach allows online parameter estimation for controlling the robot to achieve a desired position and orientation. The adaptive nature of the parameters demonstrates the robustness of the system. In contrast to existing work, the proposed technique achieves both estimation and control tasks in a single experiment. Simulation and experimental results are provided to validate the performance of the proposed scheme.

In this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a ‘partition and cover’ approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partition-boundary-induced coverage gap. The region is divided into 2D×2D grids, where D is the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property.We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy.

This paper presents the computation of the safe working zone (SWZ) of a parallel manipulator having three degrees of freedom. The SWZ is defined as a continuous subset of the workspace, wherein the manipulator does not suffer any singularity, and is also free from the issues of link interference and physical limits on its joints. The proposed theory is illustrated via application to two parallel manipulators: a planar 3-R̲RR manipulator and a spatial manipulator, namely, MaPaMan-I. It is also shown how the analyses can be applied to any parallel manipulator having three degrees of freedom, planar or spatial.

This paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dimensional problem specified in this work.

In this paper, optimal control of a 3PRS robot is performed, and its related optimal path is extracted accordingly. This robot is a kind of parallel spatial robot with six DOFs which can be controlled using three active prismatic joints and three passive rotary ones. Carrying a load between two initial and final positions is the main application of this robot. Therefore, extracting the optimal path is a valuable study for maximizing the load capacity of the robot. First of all, the complete kinematic and kinetic modeling of the robot is extracted to control and optimize the robot. As the robot is categorized as a constrained robot, its kinematics is studied using a Jacobian matrix and its pseudo inverse whereas its kinetics is studied using Lagrange multipliers. The robot is then controlled using feedforward term of the inverse dynamics. Afterward, the extracted dynamics equation of the robot is transferred to state space to be employed for calculus of variations. Considering the constrained entity of the robot, null space of the robot is employed to eliminate the Lagrange multipliers to provide the applicability of indirect variation algorithm for the robot. As a result, not only are the optimal controlling signals calculated but also the corresponding optimal path of the robot between two boundary conditions is extracted. All the modeling, controlling, and optimization process are verified using MATLAB simulation. The profiles are then double-checked by comparing the results with SimMechanics. It is proved that with the aid of the proposed controlling and optimization method of this article, the robot can be controlled along its optimal path through which the maximum load can be carried.

This paper concerns with comparing simulation studies for a newly developed concept of turning point to be used in multiple robot path planning. Different critical factors and design parameters are collected and statistical analyses are performed. After configuring different simulation scenarios, the efficient one is evaluated using a robust data envelopment analysis (RDEA). Due to uncertain aspects of various simulations scenarios, robust version of data envelopment analysis is proposed. Here, major criteria in robot path planning are deadlock and conflict avoidance, throughput, mean flow time, and effective total distance travelled. To determine the effective experiment for the proposed simulation model, RDEA is used. A comparative study with respect to different experiments having various simulation setting is developed. The results for a real robotic manufacturing cell system show effectiveness of the proposed process. Also, the efficient simulation software is determined by multiaspect analysis.

In the present work, the ankle rehabilitation robot (ARR) dynamic model that implements a new series of connection control strategies is introduced. The dynamic models are presented in this regard. This model analyzes the robot LuGre friction model and the nonlinear disturbance model. To improve the ARR system’s rapidity and robustness, a composite 2-degree of freedom (2-DOF) internal model control (IMC) controller is presented. The control performance of the compound 2-DOF IMC controller is simulated and analyzed in the present work. The simulation shows that the composite 2-DOF IMC controller has high following performance. For practical testing purposes, 1-DOF passive training and predetermined trajectory following have been completed for different swing amplitudes and frequencies. Moreover, the thrust and tension torque of the robotic dynamic and static loading characteristics are studied in active control mode. The experimental results show the effectiveness of passive training of the given trajectory and impedance training active control strategy. This paper gives the specific functions of ARR.