In this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.

As researchers have pushed the limits of what can be accomplished by a single robot operating in a known or unknown environment, a greater emphasis has been placed on the utilization of mobile multi-robotic systems to accomplish various objectives. In transitioning from a robot-centric approach to a system-centric approach, considerations must be made for the computational and communicative aspects of the group as a whole, in addition to electromechanical considerations of individual robots. This paper reviews the state-of-the-art of mobile multi-robotic system research, with an emphasis on the confluence of mapping, localization and motion control of robotic system. Methods that compose these three topics are presented, including areas of overlap, such as integrated exploration and simultaneous localization and mapping. From these methods, an analysis of benefits, challenges and tradeoffs associated with multi-robotic system design and use are presented. Finally, specific applications of multi-robotic systems are also addressed in various contexts.

A prototype robot for picking citrus is described which utilized real-time, color machine vision to vision-servo the robot on a targeted fruit. A programming technique is presented which simplified development of the task-level, robot control program. An economic evaluation of robotic harvesting in Florida determined that robotic harvesting would be approximately 50 percent more expensive than conventional hand harvesting. Harvest inefficiency was identified as the most influential factor affecting robotic harvesting economics. Field trial performance is summarized and the potential of commercial robotic harvesting discussed.

In this paper, two methods are proposed and implemented for collision detection between the robot and a human based on fuzzy identification and time series modelling. Both methods include a collision detection system for each joint of the robot that is trained to approximate the external torque. In addition, the proposed methods are able to detect the occurrence of a collision, the link that collided and to some extent the magnitude of the collision without using the explicit model of the robot. Since the speed of the detection is of critical importance for mitigating the danger, attention is paid to recognise a collision as soon as possible. Experimental results conducted with a KUKALWR manipulator using two joints in planar motion, verify the validity on both methods.

In this paper, a consensus-based control strategy is presented to gather formation for a group of differential-wheeled robots. The formation shape and the avoidance of collisions between robots are obtained by exploiting the properties of weighted graphs. Since mobile robots are supposed to move in unknown environments, the presented approach to multi-robot coordination has been extended in order to include obstacle avoidance. The effectiveness of the proposed control strategy has been demonstrated by means of analytical proofs. Moreover, results of simulations and experiments on real robots are provided for validation purposes.

The field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting.

This paper presents a novel scheme for achieving attitude control of a tumbling combination system in the post-capture phase of a tethered space robot (TSR). Given the combination rotation characteristics, tether force is applied to provide greater control torques for stabilising the attitude. The proposed control scheme involves two attitude controllers, which coordinate the controller of the tether force and thruster force and the controller of single thruster force. The numerical simulations include a comparison between this coordinated control and the traditional thruster control and a sensitivity analysis on initial values of parameters. Simulation results validate the feasibility of the attitude control scheme for a tumbling combination system, and fuel consumption of the attitude control is efficiently reduced using the coordinated control strategies.

This paper investigates the problem of concurrent mapping and localization (CML) using forward look sonar data. Results are presented from processing of an oceanic data set from an 87 kHz US Navy forward look imaging sonar using the stochastic mapping method for CML. The goal is to detect objects on the seabed, map their locations, and concurrently compute an improved trajectory for the vehicle. The resulting trajectory is compared with position estimates computed with an inertial navigation system and Doppler velocity sonar. The results demonstrate the potential of concurrent mapping and localization algorithms to satisfy the navigation requirements of undersea vehicles equipped with forward look sonar.

This paper presents the kinematics and dynamics of asix-degree-of-freedom platform-type parallel manipulator with six revolute legs,i.e. each leg consists of two links that are connected by a revolute joint.Moreover, each leg is connected, in turn, to the base and moving platforms bymeans of universal and spherical joints, respectively. We first introduce akinematic model for the manipulator under study. Then, this model is used toderive the kinematics relations of the manipulator at the displacement, velocityand acceleration levels. Based on the proposed model, we develop the dynamicsequations of the manipulator using the method of the natural orthogonalcomplement. The implementation of the model is illustrated by computersimulation and numerical results are presented for a sample trajectory in theCartesian space.

The present paper addresses the issues that should be covered in order to develop walk-through programming techniques (i.e. a manual guidance of the robot) in an industrial scenario. First, an exact formulation of the dynamics of the tool the human should feel when interacting with the robot is presented. Then, the paper discusses a way to implement such dynamics on an industrial robot equipped with an open robot control system and a wrist force/torque sensor, as well as the safety issues related to the walk-through programming. In particular, two strategies that make use of admittance control to constrain the robot motion are presented. One slows down the robot when the velocity of the tool centre point exceeds a specified safety limit, the other one limits the robot workspace by way of virtual safety surfaces. Experimental results on a COMAU Smart Six robot are presented, showing the performance of the walk-through programming system endowed with the two proposed safety strategies.

This paper addresses the map merging problem, which is the most important issue in multi-robot simultaneous localization and mapping (SLAM) using the Rao–Blackwellized particle filter (RBPF-SLAM) with unknown initial poses. The map merging is performed using the map transformation matrix and the pair of map merging bases (MMBs) of the robots. However, it is difficult to find appropriate MMBs because each robot pose is estimated under multi-hypothesis in the RBPF-SLAM. In this paper, probabilistic map merging (PMM) using the Gaussian process is proposed to solve the problem. The performance of PMM was verified by reducing errors in the merged map with computer simulations and real experiments.

General-purpose autonomous robots must have the ability to combine the available sensorimotor knowledge in order to solve more complex tasks. Such knowledge is often given in the form of movement primitives. In this paper, we investigate the problem of sequencing of movement primitives. We selected nonlinear dynamic systems as the underlying sensorimotor representation because they provide a powerful machinery for the specification of primitive movements. We propose two new methodologies which both ensure that consecutive movement primitives are joined together in a continuous way (up to second-order derivatives). The first is based on proper initialization of the third-order dynamic motion primitives and the second uses online Gaussian kernel functions modification of the second-order dynamic motion primitives. Both methodologies were validated by simulation and by experimentally using a Mitsubishi PA-10 articulated robot arm. Experiments comprehend pouring, table wiping, and carrying a glass of liquid.

This paper deals with dynamic dimensional synthesis of the Delta robot using the pressure/transmission angle constraints. Two types of pressure/transmission angles are defined, with which the direct and indirect singularities can be identified in a straightforward manner. Two novel global dynamic metrics are proposed for minimisation, which are associated respectively with the inertial and centrifuge/Coriolis components of the driving torque. Various geometrical and performance constraints are taken into account in terms of workspace/machine volume ratio, pressure/transmission angles, etc. The effects of pressure/transmission angle constraints on the feasible domain of design variables are investigated in depth via an example, and a set of optimised dimensional parameters is obtained for achieving a good kinematic and dynamic performance throughout the entire task workspace.

Interpolation of a robot joint trajectory is realized using trigonometric splines. This original application has several advantages over existing methods (e.g. those using algebraic splines). For example, the computational expense is lower, more constraints can be imposed on the trajectory, obstacle avoidance can be implemented in real time, and smoother trajectories are obtained. Some of the spline parameters can be chosen to minimize an objective function (e.g. minimum jerk or minimum energy). If jerk is minimized, the optimization has a closed form solution. This paper introduces a trajectory interpolation algorithm, discusses a method for path optimization, and includes examples.

In this paper we propose an analytical formulation for simulation and design of a one d.o.f. articulated finger mechanism with three phalanges. The formulation is based on a study of the design and operation of an index human finger. In particular, we have proposed a suitable mechanical design for an anthropomorphic finger as both an approximation of human architecture and an easy practical design. Kinematic characteristics are illustrated with numerical examples.

This paper presents a novel control algorithm for electrically driven robot manipulators. The proposed control law is simple and model-free based on the voltage control strategy with the decentralized structure and only joint position feedback. It works for both repetitive and non-repetitive tasks. Recently, some control approaches based on the uncertainty estimation using the Fourier series have been presented. However, the proper value for the fundamental period duration has been left as an open problem. This paper addresses this issue and intuitively shows that in order to perform repetitive tasks; the least common multiple (LCM) of fundamental period durations of the desired trajectories of the joints is a proper value for the fundamental period duration of the Fourier series expansion. Selecting the LCM results in the least tracking error. Moreover, the truncation error is compensated by the proposed control law to make the tracking error as small as possible. Adaptation laws for determining the Fourier series coefficients are derived according to the stability analysis. The case study is an SCARA robot manipulator driven by permanent magnet DC motors. Simulation results and comparisons with a voltage-based controller using adaptive neuro-fuzzy systems show the effectiveness of the proposed control approach in tracking various periodic trajectories. Moreover, the experimental results on a real SCARA robot manipulator verify the successful practical implementation of the proposed controller.

The paper focuses on the problem of point-to-point trajectory planning for flexible redundant robot manipulators (FRM) in joint space. Compared with irredundant flexible manipulators, a FRM possesses additional possibilities during point-to-point trajectory planning due to its kinematics redundancy. A trajectory planning method to minimize vibration and/or executing time of a point-to-point motion is presented for FRMs based on Genetic Algorithms (GAs). Kinematics redundancy is integrated into the presented method as planning variables. Quadrinomial and quintic polynomial are used to describe the segments that connect the initial, intermediate, and final points in joint space. The trajectory planning of FRM is formulated as a problem of optimization with constraints. A planar FRM with three flexible links is used in simulation. Case studies show that the method is applicable.

Parallel mechanisms possess several advantages such as the possibilities for high acceleration and high accuracy positioning of the end effector. However, most of the proposed parallel manipulators suffer from a limited workspace. In this paper, a novel 6-DOF parallel manipulator with coaxial actuated arms is introduced. Since parallel mechanisms have more workspace limitations compared to that of serial mechanisms, determination of the workspace in parallel manipulators is of the utmost importance. For finding position, angular velocity, and acceleration, in this paper, inverse and forward kinematics of the mechanism are studied and after presenting the workspace limitations, workspace analysis of the hexarot manipulator is performed by using MATLAB software. Next, using the obtained cloud of points from simulation, the overall borders of the workspace are illustrated. Finally, it is shown that this manipulator has the important benefits of combining a large positional workspace in relation to its footprint with a sizable range of platform rotations.

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.

This paper presents a dynamic model of a planar parallel manipulator including structural flexibility of several linkages. The equations of motion are formulated using the Lagrangian equations of the first type and Lagrangian multipliers are introduced to represent the geometry of multiple closed loop chains. Then, an active damping approach using a PZT actuator is described to attenuate structural vibration of the linkages. Overall dynamic behavior of the manipulator, induced from structural flexibility of the linkage, is well illustrated through simulations. This analysis will be used to develop a prototype parallel manipulator.