Maximum Dynamic Load-carrying Capacity Research Articles

Close-in blast performance of RC beams with varying cross-sectional sizes and damage assessment based on the dynamic load carrying capacity

Aiming to investigate the impact of cross-sectional size on the close-in blast performance of reinforced concrete (RC) beams, the local damage features and structural responses of RC beams with varying cross-sectional widths and depths subjected to close-in explosions were investigated experimentally and numerically. Experiments were conducted on ten RC beams with a cross-sectional size of 25 cm × 12.5 cm to study the influence of scaled distance and charge mass on the local damage size and structural responses (displacement and reaction force). The results indicate that the blasting face damage to tested beams, such as the crushing crater of concrete cover and side peeling-off damage, is more sensitive to the scaled distance (intensity of applied blast loads) compared to the rear spalling damage. And the peak reaction force exhibited a gradual increase within the range of 0.5 m/kg1/3, eventually reaching an average maximum value of 93.5 kN, and remained unchanged after the scaled distance was greater than 0.5 m/kg1/3, indicating that the dynamic load-carrying capacity of tested beams increased with the increasing scaled distance and the maximum dynamic load-carrying capacity of these beams is around 93.5 kN. Furthermore, a numerical model of RC beams subjected to close-in explosions was established based on the Fluid-Structure-Interaction (FSI) method. For validation, the local damage morphology and sizes of RC beams and their dynamic responses (displacement and reaction force) were computed and compared with the corresponding experimental results, concluding that the numerical results are in good agreement with the experimental findings. A parametric analysis was conducted to study the impact of cross-sectional size on the development process of initial local damage, stress distribution in cross sections, local damage sizes, and structural responses. Moreover, a damage index of the dynamic load-carrying capacity was derived from the relationship between the normalized maximum reaction force and the normalized maximum displacement to assess the damage degree of RC beams with varying cross-sectional sizes. This work can serve as a partial reference for relevant research.

Motion equations of cooperative multi flexible mobile manipulator via recursive Gibbs–Appell formulation

• The model of time-varying multi flexible mobile manipulators is proposed. • The cooperative mobile manipulator with the closed kinematic chain is illustrated. • The Gibbs–Appell formulation has been upgraded by using Lagrange multiplayer. • The maximum dynamic load caring capacity has been evaluates for a common object. In this paper, the developed model of an N-flexible-link mobile manipulator with revolute-prismatic joints is presented for the cooperative flexible multi mobile manipulator. In this model, the deformation of flexible links is calculated by using the assumed modes method. In additions, non-holonomic constraints of the robots’ mobile platforms that bound its locomotion are considered. This limitation is alleviated through the concurrent motion of revolute and prismatic joints, although it results in computational complexity and changes the final motion equations to time-varying form. Not only is the proposed dynamic model implemented for the multi-mobile manipulators with arms having independent motion, but also for multi-mobile manipulators in cooperation after defining gripper's kinematic constraints. These constraints are imported to the dynamic equations by defining Lagrange multipliers. The recursive Gibbs–Appell formulation is preferred over other similar approaches owing to the capability of solving the equations without the need to use Lagrange multipliers for eliminating non-holonomic constraints in addition to the novel optimized process of obtaining system equations. Hence, cumbersome simultaneous computations for eliminating the constraints of platform and arms are circumvented. Therefore, this formulation is improved for the first time by importing Lagrange multipliers for solving kinematic constrained systems. In the simulation section, the results of forward dynamics solution for two flexible single-arm manipulators with revolute-prismatic joints while carrying a rigid object are presented. Inverse dynamics equations of the system are also presented to obtain the maximum dynamic load-carrying capacity of the two-rigid-link mobile manipulators on a predefined path. Two constraints, namely the capacity of joint motors torque and robot motion stability are considered as the limitation criteria. The concluded motion equations are used to accurately control the movement of sensitive bodies, which is not achievable through the use of one platform.

A New Methodology for Solving Trajectory Planning and Dynamic Load-Carrying Capacity of a Robot Manipulator

A new methodology using a direct method for obtaining the best found trajectory planning and maximum dynamic load-carrying capacity (DLCC) is presented for a 5-degree of freedom (DOF) hybrid robot manipulator. A nonlinear constrained multiobjective optimization problem is formulated with four objective functions, namely, travel time, total energy involved in the motion, joint jerks, and joint acceleration. The vector of decision variables is defined by the sequence of the time-interval lengths associated with each two consecutive via-points on the desired trajectory of the 5-DOF robot generalized coordinates. Then this vector of decision variables is computed in order to minimize the cost function (which is the weighted sum of these four objective functions) subject to constraints on joint positions, velocities, acceleration, jerks, forces/torques, and payload mass. Two separate approaches are proposed to deal with the trajectory planning problem and the maximum DLCC calculation for the 5-DOF robot manipulator using an evolutionary optimization technique. The adopted evolutionary algorithm is the elitist nondominated sorting genetic algorithm (NSGA-II). A numerical application is performed for obtaining best found solutions of trajectory planning and maximum DLCC calculation for the 5-DOF hybrid robot manipulator.

Trajectory planning of parallel kinematic manipulators for the maximum dynamic load-carrying capacity

The objective of this paper is to identify the trajectory that accomplishes the assigned motion with the maximum dynamic load-carrying capacity (DLCC) which is subject to constraints imposed by the kinematics and dynamics of a manipulator structure. In this study, the possible trajectories of the manipulator are modeled using a parametric path representation, the optimal trajectory is then obtained using a two-loop of optimization process, in which the inner-loop optimization process based on the Simplex-type linear programming method is used to determine the dynamic loading at each discrete point along the presumed trajectory and then to formulate the DLCC; the outer-loop optimization process based on the particle swarm optimization algorithm is to solve the controlled points by maximizing the formulated DLCC. The numerical results confirm the feasibility of the optimized trajectories and demonstrate the effectiveness of the proposed algorithm for the maximum dynamic load-carrying trajectory planning of a parallel kinematic manipulator.

Optimal path planning of a cable-suspended robot with moving boundary using optimal feedback linearization approach

In this paper, the optimal path of a cable-suspended robot between two boundaries is assessed subject to its maximum load, while the initial and target points are moving boundary. Considering the fact that the most important application of cable robots is load carrying between two boundaries, planning the optimal path in which the heaviest load can be carried is extremely applicable. A closed loop optimal path planning algorithm is proposed in this paper for non-linear dynamic of a cable robots based on optimal feedback linearization. This method not only produces the optimal path of the end-effector, but also is robust to external disturbances and parametric uncertainties as a result of its closed loop nature. Moreover, considering the fact that in many automation applications the target of load handling is a sort of moving boundary like conveyors, finding the optimal point of this boundary which produces the optimal path with the maximum dynamic load carrying capacity (DLCC) amongst the other points of the boundary is obviously a useful study. Therefore, an online solution, based on variational algorithm is proposed here to solve the moving boundary problem which is compatible with the presented closed loop optimal path planning. This method is developed for both the initial and final moving boundaries. Finally maximum DLCC is obtained using an iterative method to check the optimality of the proposed method. The efficiency of the proposed algorithm is verified at the end by the aid of some simulation scenarios performed on a spatial six DOFs cable robot with six cables. The motors’ torque, motors’ speed, and resultant DLCCs are calculated for both simple optimal path and moving boundary cases and comparison of the results proves the mentioned claims based on superiority of the proposed algorithm of moving boundary in saving the energy and increasing the DLCC. All simulation results are supported by conducting an experimental study on the cable robot of IUST (ICaSbot) for regulation movement of the end-effector with moving boundary.

Dynamic load-carrying capacity of a novel redundantly actuated parallel conveyor

Conveyors are important equipment in the painting shop. Conveyors with cantilever beams have low load-carrying capacity and can carry small cars. To solve this problem, this paper presents a novel conveyor that uses redundantly actuated parallel manipulators. A method is proposed to obtain the maximum dynamic load-carrying capacity of the conveyor by optimizing the internal forces of the redundantly actuated parallel manipulators. To improve the dynamic load-carrying capacity, approaches using counterweights are utilized and compared. Furthermore, the maximum dynamic load-carrying capacity of the redundant parallel manipulator is compared with that of its nonredundant counterpart.

Optimal regulation of a cable robot in presence of obstacle using optimal adaptive feedback linearization approach

SUMMARYOptimal 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.

Trajectory planning of mobile manipulators using dynamic programming approach

SUMMARYThis paper presents a solution for optimal trajectory planning problem of robotic manipulators with complicated dynamic equations. The main goal is to find the optimal path with maximum dynamic load carrying capacity (DLCC). Proposed method can be implemented to problems of both motion along a specified path and point-to-point motion. Dynamic Programming (DP) approach is applied to solve optimization problem and find the positions and velocities that minimize a pre-defined performance index. Unlike previous attempts, proposed method increases the speed of convergence by using the sequential quadratic programming (SQP) formulation. This formulation is used for solving problems with nonlinear constraints. Also, this paper proposes a new algorithm to design optimal trajectory with maximum DLCC for both fixed and mobile base mechanical manipulators. Algorithms for DLCC calculations in previous works were based on indirect optimization method or linear programming approach. The proposed trajectory planning method is applied to a linear tracked Puma and the mobile manipulator named Scout. Application of this algorithm is confirmed and simulation results are compared with experimental results for Scout robot. In experimental test, results are obtained using a new stereo vision system to determine the position of the robot end-effector.

Reconfiguration of a parallel kinematic manipulator for the maximum dynamic load-carrying capacity

In determining the maximum dynamic load-carrying capacity (DLCC) of reconfigurable motor-driven parallel kinematic manipulators (PKM), the objective is to identify the optimal configuration which accomplishes the assigned motion for the maximum DLCC subject to the constraints imposed by the kinematics and dynamics of the manipulator structure. In this study, the maximum DLCC problem of a reconfigurable PKM is formulated using the structured Boltzmann–Hamel–d'Alembert formulism, and then the optimal reconfiguration is obtained using a two-level of optimization process, in which the particle swarm optimization (PSO) algorithm is for the higher-level optimization and the Simplex-type linear programming (LP) method is for the lower-level optimization, such that the reconfiguration is achieved by re-locating the base points along linear guideways. The numerical results present the effects of the base locations on the DLCC and the corresponding kinematics and dynamics along the prescribed trajectory.

Maximum DLCC of Spatial Cable Robot for a Predefined Trajectory Within the Workspace Using Closed Loop Optimal Control Approach

One of the most important applications of cable robots is load carrying along a specific path. Control procedure of cable robots is more challenging compared to linkage robots since cables can't afford pressure. Meanwhile carrying the heaviest possible payload for this kind of robots is desired. In this paper a nonlinear optimal control is proposed in order to control the end-effector within a predefined trajectory while the highest Dynamic Load Carrying Capacity (DLCC) can be carried. This purpose is met by applying optimum torque distribution among the motors with acceptable tracking accuracy. Besides, other algorithms are applied to make sure that the allowable workspace constraint is also satisfied. Since the dynamics of the robot is nonlinear, feedback linearization approach is employed in order to control the end-effector on its desirable path in a closed loop way while Linear Quadratic Regulator (LOR) method is used in order to optimize its controlling gains since the state space is linearized by the feedback linearization. The proposed algorithm is supported by doing some simulation studies on a two Degrees of Freedom (DOF) constrained planar cable robot as well as a six DOFs under constrained cable suspended robot and their DLCCs are calculated by satisfying the motor torque, tracking error and allowable workspace constraints. The results including the angular velocity, motors' torque, actual tracking of the end-effector and the DLCC of the robot are calculated and verified using experimental tests done on the cable robot. Comparison of the results of open loop simulation results, closed loop simulation results and experimental tests, shows that the results are improved by applying the proposed algorithm. This is the result of tuning the motors' torque and accuracy in a way that the highest DLCC can be achieved.

Dynamic load-carrying capacity of cable-suspended parallel manipulators

In this paper, a computational method for obtaining maximum dynamic load-carrying capacity (DLCC) of the cable-suspended parallel manipulator is developed. The manipulator is assumed to be a rigid manipulator and the joint actuator torque and cable tension force capacities are considered as the major limiting factors in determining the maximum payload. The maximum dynamic payload-carrying capacity of the manipulator is established while the dynamic model of a typical actuator system is used in the joint actuator force capacity for a given trajectory. The proposed constraint completes a common framework for dynamic characterization of a cable-suspended parallel robot. The 3 and 6 degrees of freedom cable robots are presented for computing the maximum DLCC.

Optimal actuator sizing and end point deformation for mobile robotic manipulators with elastic joint based on load criteria

Application of a numerical method to determine the maximum dynamic load carrying capacity (DLCC) for a robotic manipulator carrying an automobile petrol tank with joint elasticity subject to accuracy and actuator constraints is described in this paper. The maximum DLCC which can be achieved by a manipulator during a given trajectory is limited by a number of factors. The most important of which are the dynamic specification of the manipulator, the actuator limitations, and the elasticity of the joints such as reducers and servo drive system. Initially, the kinematic equations for carrying the automobile petrol tank by the robotic arm with 6 degrees of freedom (DOF) were calculated. The mechanical modeling was achieved via software programming. Dynamic modeling of the flexible joints manipulator was done simply for the three major axes. A method for determination of the dynamic load capacity with specific reference to both accuracy and actuator constraints is explained. The results obtained indicate the importance of both constraints and which constraint is more critical for accuracy and tracking.