Optimal path planning of a closed loop cable robot, between two predefined points in presence of obstacles is the goal of this paper. This target is met by proposing a new method of optimal regulation for non linear systems while Dynamic Load Carrying Capacity (DLCC) of the robot is supposed as the related cost function. Feedback linearization is used to linearize the system while Linear Quadratic Regulator (LQR) is employed to optimize the DLCC of the system based on torque and error constraints. Obstacle avoidance for both the end-effector and cables is also considered by the aid of designing an adaptive local obstacle avoidance controller. As a result of linearized nature of the proposed optimal regulation and obstacle avoidance, fast calculation for real time applications is possible. Therefore, formulation of the optimal feedback linearization, together with calculating the DLCC of the robot based on the presented constraints is derived. Finally, a simulation study is performed to study the optimal dynamics and also the maximum DLCC of the cable robot in presence of obstacles. Simulation results are eventually compared with experimental tests conducted on IUST Cable Suspended Robot (ICaSbot) to verify the validity and efficiency of the proposed optimal controllers.

This paper describes the design and experimental validation of an omnidirectional unmanned ground vehicle built for operation on real-world, unstructured terrains. The omnidirectional capabilities of this robot give it advantages over skid-steered or Ackermann-steered vehicles in tight and confined spaces. The robot's conventional wheels allow for operation in natural, outdoor environments as compared to omnidirectional robots that use specialized wheels with small, slender rollers and parts that can easily become obstructed with debris and dirt. Additionally, the robot's active split offset caster design allows the robot to kinematically follow continuous but non-differentiable paths and heading angles regardless of its current kinematic configuration. The active split offset caster design also results in less scrubbing torque and therefore less energy consumption during steering as compared to actively steered caster designs. The focus of this paper is the robot's mechanical design as it relates to kinematic isotropy and experimental validation of the design.

This paper presents a new genetic algorithm methodology to solve the trajectory planning problem. This methodology can obtain smooth trajectories for industrial robots in complex environments using a direct method. The algorithm simultaneously creates a collision-free trajectory between initial and final configurations as the robot moves. The presented method deals with the uncertainties associated with the unknown kinematic properties of intermediate via points since they are generated as the algorithm evolves looking for the solution. Additionally, the objective of this algorithm is to minimize the trajectory time, which guides the robot motion. The method has been applied successfully to the PUMA 560 robotic system. Four operational parameters (execution time, computational time, end-effector distance traveled, and significant points distance traveled) have been computed to study and analyze the algorithm efficiency. The experimental results show that the proposed optimization algorithm for the trajectory planning problem of an industrial robot is feasible.

Segway is a self-balancing motorized two-wheeled vehicle which is able to carry the human body. The main issue in design of such a vehicle is to choose a stable control system capable of keeping the rider close to the upright position over smooth and non-smooth surfaces. This work extends the research previously performed by the authors for design of a controller, using the feedback linearization technique, to increase the stability of a two-wheeled vehicle carrying human. This paper investigates the design and validation of a controller for an inertial mobile vehicle using the Lyapunov's feedback control design technique. The system equations of motion are derived followed by finding the Lyapunov function required to design the controller. Owing to the discontinuity, originating from a sign function in the control law, the proposed control system is discontinuous. Therefore, the existence, continuity, and uniqueness of the solution are proven utilizing the Filippov's solution. Afterwards, the asymptotic stability of the control system is proven using the extensions of Lyapunov's stability theory to nonsmooth systems, and LaSalle's invariant set theorem. Finally, the effectiveness of the proposed control system is validated using simulation studies. Results confirm that the controller keeps the system stable while provides good position tracking responses.

In this research, a Stewart parallel platform with rotary actuators is simulated and a prototype is tested under different operative conditions. The purpose is to make the robot robust against inertia variations considering the fact that different payloads of unknown size may be transported. Due to the complexity issued by expressing the equations of motion with independent variables, the governing equations are derived by Lagrange's method using Lagrange multipliers for imposing the kinematic constraints imposed on this parallel robot. Eliminating Lagrange multipliers by projecting the equations onto the orthogonal complement of the space of constraints, the equations of motion are transformed to a reduced form suitable for the purpose of controller design. The control approach considered here is based on a neuro-fuzzy interference method. As a first step, each revolute arm link are individually trained under different loadings and diverse maneuvers. It is purposed that once employed together, the links will have learned how to collaborate with each others for performing a common task. Training data are divided to several clusters by using a subtractive clustering algorithm. For every cluster, a fuzzy rule is derived so that the output follows the desired trajectory. In the last stage, these rules are employed by utilizing back propagation algorithms and the effectiveness of the neuro-fuzzy system becomes approved by performing multiple maneuvers and its robustness is checked under various inertia loads. The controller has ultimately been implemented on a prototype of the Stewart mechanism in order to analyze the reliability and feasibility of the method.

The increasing importance of computational models for the design of complex mechanical systems raises a discussion on defining some criteria for the selection of adequate modelling methods. This paper aims to contribute to such discussion from an educational point of view. By choosing the Delta parallel mechanism as a typical representative of multi-body mechanical systems, four approaches – one based on the Principle of Virtual Work, two based on Lagrange's formalism, and one based on Kane's formalism – are analysed from the perspective of modelling procedures. Finally, inverse dynamic simulations are carried out along with qualitative comparisons of the considered approaches.

The paper discusses the shape control problem related to a class of hyper-redundant robot arms with continuum elements, i.e. tentacle arms. A spatial weighted technique for sensor measurements is used in order to facilitate the parameter estimation. The paper focuses on the shape control by using the curvature gradient a constant parameter along the segment arm. The conditions that ensure a constant curvature gradient for a class of tentacle arms characterized by elastic backbone are determined. A sensor network distributed along the robot arm is used for the shape control. The main parameters of the arm shape, curvature and curvature gradient or “shape” Jacobian for the control problem are estimated. Two measuring systems are used: a) a distributed angle sensor network and b) a curvature sensor placed at the end of the arm segment. The stability analysis and the resulting controllers are obtained using the concept of boundary geometric control and the weighted state control methods. The shape control algorithms for dynamic models with uncertain components are proposed. Numerical simulations and experimental results illustrate the effectiveness of the above mentioned algorithms.

As a fatigable part of industrial products, cable is a valuable research topic to predict the product lifetime. Since the cable fatigue is related to the bending radius in motion, this paper presents a cable motion capture and bending radius calculation method based on optical tracking system. In particular, a marker sorting algorithm is developed for further spline interpolation in cable motion tracking. The cable bending radius calculation precision is analysed as well. The Results show that the proposed method can successfully track cable motion with an acceptable error for cable lifetime prediction.

Teleoperated grasping requires the abilities to follow the intended trajectory from the user and autonomously search for a suitable pre-grasp pose relative to the object of interest. Challenges include dealing with uncertainty due to the noise of the teleoperator, human elements and calibration errors in the sensors. To address these challenges, an effective and robust algorithm is introduced to assist grasping during teleoperation. Although without premature object contact or regrasping strategies, the algorithm enables the robot to perform online adjustments to reach a pre-grasp pose before final grasping. We use three infrared (IR) sensors that are mounted on the robot hand, and design an algorithm that controls the robot hand to grasp objects using the information from the sensors' readings and the interface component. Finally, a series of experiments demonstrate that the system is robust when grasping a wide range of objects and tracking slow-moving mobile objects. Empirical data from a five-subject user study allows us to tune the relative contributions from the IR sensors and the interface component so as to achieve a balance of grasp assistance and teleoperation.

In this paper, a novel control scheme is proposed to guarantee position and force tracking in nonlinear teleoperation systems subject to varying communication delays. Stability and tracking performance of the teleoperation system are proved using a proposed Lyapunov–Krasovskii functional. To show its effectiveness, the teleoperation controller is simulated on a pair of planar 2-DOF (degree of freedom) robots and experimented on a pair of 3-DOF PHANToM Premium 1.5A robots connected via a communication channel with time-varying delays. Both the planar robots in simulations and the PHANToM robots in experiments possess nonlinear dynamics.

This paper presents a path planning algorithm for autonomous navigation of non-holonomic mobile robots in complex environments. The irregular contour of obstacles is represented by segments. The goal of the robot is to move towards a known target while avoiding obstacles. The velocity constraints, robot kinematic model and non-holonomic constraint are considered in the problem. The optimal path planning problem is formulated as a constrained receding horizon planning problem and the trajectory is obtained by solving an optimal control problem with constraints. Local minima are avoided by choosing intermediate objectives based on the real-time environment.