Optimal Load Distribution for Two Industrial Robots Handling a Single Object

The load distribution problem for two coordinating industrial robots handling a single object is studied. When two industrial robots grasp a single object, the total number of degrees of freedom is usually greater than six. Thus, the joint torques of two robots for a required motion of the object is not unique. The redundant degrees of freedom may be used to optimize certain kind of performance. The least energy consumption is selected as the optimization criterion. Optimal algorithms without and with a bound on the joint torques are investigated. The results show that the algorithms are computationally complicated and not suitable for real-time applications. Alternatively, optimal algorithms are proposed for load distribution with minimum exerted forces on the object. These algorithms require less computational time, which makes them attractive for real-time applications. >

Precise position force control is the core and difficulty of robot technology, especially for robots with redundant degrees of freedom. For example, track-based controls often fail to grind the robot due to the intolerable impact force applied to the end-effector. The main difficulties lie in the coupling of motion and contact forces, redundancy analysis and physical constraints. In this chapter, we propose a new motion force control strategy under the framework of recursive neural network. The tracking error and contact force are described respectively in the orthogonal space. By choosing the minimum joint torque as the secondary task, the control problem is transformed into the QP problem under multi-constraint conditions. In order to obtain real-time optimization of the joint torque relative to the non-convex joint angle, the original QP is reconstructed at the velocity level, and the original objective function is replaced by the time derivative. Then a convergent dynamic neural network is established to solve the improved QP problem online. The robot position control based on recursive neural network is extended to the robot position control based on position force, which opens a new way for the robot to turn from simple control angle to crossover design with convergence and optimality. Numerical results show that the proposed method can realize precise position force control, deal with inequality constraints such as joint angular velocity and torque limitation, and reduce joint torque consumption by 16% on average.

End-effector self-reconfiguring live working robot autonomously replaces the end actuator of the robot arm for different tasks, and at the same time completes high-altitude work tasks according to the designed motion plan. In order to ensure uninterrupted operation on power transmission lines, the research methods to enhance the efficiency of robot arm work while reducing energy consumption is of significant importance. Based on this, the paper proposes a time-energy optimal trajectory optimization method for a six-joint robotic arm of a reconfigurable robot for power line maintenance based on Fourier series, taking into account the influence of both robot kinematics and dynamics on the motion trajectory, the positions, velocities, accelerations and joint torques are all considered as constraints. Time and energy are chosen as objective functions. By utilizing the Lagrangian equation method establish an energy-based robot dynamic model in generalized coordinates. The motion curves of each joint are obtained by the cubic spline interpolation method in the joint space, and the boundary conditions of the motion curves are optimized by Fourier series, so as to establish the optimal trajectory mathematical model. The time-energy optimal trajectory planning problem is transformed into a convex optimization problem, the DTW (Dynamic Time Warping) algorithm is used to regularize the difference between the time function and the energy function in the displacement sequence under the known trajectory, and the number of terms in the Fourier series of the minimum hour of the objective function is obtained by the GA-DQN (Genetic Algorithm-Deep Q-Network) algorithm. Finally, with the actuator replacement at the end of the robotic arm as the operation objective, the time-energy optimal trajectory is obtained from the desired positional attitude at the initial and termination of the end of the robotic arm. Simulation analysis is carried out under MATLAB and compared with the motion trajectory obtained by the fifth degree polynomial interpolation method, and the results show that by optimizing the motion trajectory with Fourier series, the motion time is shortened by about 35.67% and the energy consumption is reduced by about 39.63%. And online experiments were conducted in the field to measure the output errors of joints 2 and 5, as well as the actual energy and time consumption of outputting the optimal trajectories, which verified that the trajectories obtained under this optimization algorithm have better performance and can meet the requirements of online robot end self-reconfiguration operation.

This paper presents a new dynamic programming (DP) approach to the optimal path following problem. The method rests on an interpolation in the phase plane so that the resulting joint accelerations and joint torques are continuous. This allows for taking into account limits on the joint jerks and torque rates in addition to joint velocities, accelerations, torques, and the mechanical power. Most methods proposed in the literature yield values of optimal trajectories at discrete sampling times so that this must be interpolated subsequently to the trajectory optimization. This causes violations of the joint jerk and torque rate limits. The proposed method does not suffer from this problem, which is a main feature of this approach. Unlike most of the previously proposed methods, joint jerk and torque rate limits are addressed with a reasonable increase in the computation time of DP algorithms. Special attention is given to experimental validation of the optimization results. The presented experimental results confirm the importance of taking the motor torque characteristics as well as the Coulomb and viscous friction into account. Neglecting these effects (as most previous publications did) leads to trajectories that cannot be performed by real manipulators. Apart from DP, other approaches with smaller computation times exist. However, most of these methods are either limited to the time optimal case (which might not always be the desired criterion) and are not able to handle all earlier mentioned constraints or cannot take into account viscous friction effects. Apart from that a sequential convex programming (SCP) approach exists, which accounts for the same constraints as the presented approach. Therefore, the proposed DP approach is compared to this SCP method and an example is presented where the time optimal trajectory is performed by a real manipulator.

Industrial robots are required to interact with the surrounding environment to perform a given task (e.g., an assembly task). However, standard industrial robots are commonly position-controlled. Therefore, there is the need to implement an outer compliance controller to guarantee a safe interaction. Such compliance controllers require force/torque measurements to close the loop, and most of the industrial manipulators available on the market do not have embedded force/torque sensor(s), requiring additional efforts (i.e., additional costs and implementation resources) for such integration in the robotic setup. To provide a standard industrial sensorless position-controlled robot with the capabilities to execute an interaction task, the proposed paper defines an external joint torques observer for the implementation of an outer sensorless compliance controller. More in detail, exploiting the resulting position-controlled robot dynamics, an Extended Kalman Filter (EKF) is proposed to estimate the external joint torques. Exploiting such an estimation, an outer impedance controller can be designed, providing a position/velocity reference to the inner position controller. The described approach has been validated with experiments on a Franka EMIKA panda robot. A human operator interacts with the controlled robot, applying external wrenches (i.e., both Cartesian forces and torques). The resulting external joint torques are estimated making use of the proposed EKF, comparing the achieved results with the signals provided by the joint torque sensors of the Franka EMIKA panda robot (measurements used as a baseline for validation purposes). Experimental results show the capabilities of the proposed control framework in estimating the applied external joint torques while implementing a position control-based compliance controller.

Demands have been increasing for skillful robots in many fields including atomic power generation, ocean bottom development, space craft, hospital aids, and so on. Such robots will essentially require high degrees of freedom. This paper discusses the advantages of a robot equipped with redundant degrees of freedom and outlines a method of positive use of such redundancy.

Robots often switch from highly dynamic motion to delivering high torques at low speeds. The actuation requirements for these two regimes are very different. As a consequence, the average efficiency of the actuators is typically much lower than the efficiency at the optimal working point. A potential solution is to use multiple motors for a single motor joint. This results in a redundant degree of freedom, which can be exploited to make the system more efficient overall. In this work, we explore the potential of kinematically redundant actuators in dynamic applications. The potential of a kinematically redundant actuator with two motors is evaluated against a single-motor equivalent in terms of operating range, maximum acceleration, and energy consumption. We discuss how the comparison is influenced by the design of the actuator and the way how the power is distributed over the input motors. Our results support the idea that kinematically redundant actuators can resolve the conflicting torque–speed requirements typical of robots.

The paper presents an algorithm of managing the units of equipment for burning fuel of different calorific capacities in steam boilers DKVR No 20/13. Burning such kinds of fuel enables an enterprise both to reduce fuel consumption and to decrease burning expenses 13 to 19 times. The three algorithms considered in the research were: an equel distribution of the load between three boilers, between two boilers while the third one remains in reserve, and the boilers operation at the maximum load. The optimal algorithm was based on the criterion that accounted for such factors as equipment reliability and the efficiency of its use. The reliability indicator was based on such parameters as the entire time of the equipment performance, different load rates of the equipment, and the performance time beyond the rated operation. The optimal criterion was calculated by the method of dynamic programming. The criterion determined the optimal algorithm for the equipment performance with each kind of fuel. The research has shown that the suggested method for determining the optimal load management algorithm facilitates an efficient steam boilers operation. Consequently, the use of uncertified fuel allows to decrease the use of certified (purchased) fuel and to reduce the expenditure.

In this study, a strategy based on the particle swarm optimization (PSO) algorithm is developed to control the microstructures of a Ni-based superalloy during hot forging. This strategy is composed of three parts, namely, material models, optimality criterions, and a PSO algorithm. The material models are utilized to predict microstructure information, such as recrystallization volume fraction and average grain size. The optimality criterion can be determined by the designed target microstructures and random errors. The developed strategy is resolved using the PSO algorithm, which is an intelligent optimal algorithm. This algorithm does not need a derivable objective function, which renders it suitable for dealing with the complex hot forging process of alloy components. The optimal processing parameters (deformation temperature and strain rate) are obtained by the developed strategy and validated by the hot forging experiments. Uniform and fine target microstructures can be obtained using the optimized processing parameters, which indicates that the developed strategy is effective for controlling the microstructural evolution during the hot forging of the studied superalloy.

Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. One of the major sources of uncertainty is the one associated with industrial robot geometrical parameter values. In this paper, using multi-objective meta-heuristic optimization approaches and optical metrology measurements, more accurate Denavit–Hartenberg (DH) geometrical parameters of an industrial robot are estimated. The sensor data used to perform this calibration are the absolute 3D position readings using a highly accurate laser tracker (LT) and industrial robot joint angle readings. Other than position accuracy, the mean absolute deviation of the DH parameters from the manufacturer’s given parameters is considered as the second objective function. Therefore, the optimization problem investigated in this paper is a multi-objective one. The solution to the multi-objective optimization problem is obtained using different evolutionary and swarm optimization approaches. The evolutionary optimization approaches are nondominated sorting genetic algorithms and a multi-objective evolutionary algorithm based on decomposition. The swarm optimization approach considered in this paper is multi-objective particle swarm optimization. It is observed that NSGAII outperforms the other two optimization algorithms in terms of a more diverse Pareto front and the function corresponding to the positional accuracy. It is further observed that through using NSGAII for calibration purposes, the root mean squared for positional error has been improved significantly compared with nominal values.

This study aims to illustrate a sequence that optimizes the flight-path trajectory for a hybrid-electric aircraft at mission level, in addition to identifying the respective optimum power management strategy. An in-house framework for hybrid-electric propulsion system modeling is utilized. A hybrid-electric commuter aircraft serves as a virtual test-bench. Vectorized calculations, decision variable count, and optimization algorithms are considered for reducing the computational time of the framework. Performance improvements are evaluated for the aircraft's design mission profile. Total energy consumption is set as the objective function. Emphasis lies on minimizing the average value and standard deviation of the energy consumption and timeframe metrics. The best performing application decreases computational time by two orders of magnitude, while retaining equal accuracy and consistency as the original model. It is employed for creating a dataset for training an artificial neural network (ANN) against random mission patterns. The trained network is integrated into a surrogate model. The latter part of the analysis evaluates optimized mission profile characteristics with respect to energy consumption, against a benchmark flight-path. The combined optimization process decreases the multihour-scale timeframe by two orders of magnitude to a 3-min sequence. Using the novel framework, a 12% average energy consumption benefit is calculated for short, medium, and long regional missions, against equivalent benchmark profiles.

Reinforcement learning is very effective for robot learning. Because it does not need priori knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforce learning algorithm: “Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA) ”. It is designed for complicated systems with large action-state space like a robot with many redundant degrees of freedom. However the application of QDSEGA is restricted to static systems. We extend the layered structure of QDSESA so that it could be applicable to the dynamical system. A snake-like robot has many redundant degrees of freedom and the dynamics of the system are very important to complete the locomotion task. For this task, application of usual reinforcement learning is difficult. In this paper, we extend layered structure of QDSEGA for applying real robot. We apply it to acquiring of locomotion pattern of the snake-like robot and demonstrate the validity of QDSEGA with the extended layered structure by simulation and experiment.

The human body can be modeled as a kinematically redundant manipulator which exploits "redundant degrees of freedom" to execute various motions in a suitable fashion. Differently from the typical kinematically redundant robots that are attached to the fixed ground, the zero moment point (ZMP) condition should be taken into account not to fall down. Thus, this paper investigates a motion planning algorithm for kinematically redundant manipulator standing on the ground. For this, a geometric constraint equation is derived from the existing ZMP equation. This constraint equation is formed like a second-order kinematic equation, which enables one to plan the ZMP trajectory in a feed-forward fashion. This constraint equation and the kinematic equation of the manipulator model are solved together. Then, the solution of this composite equation guarantees both the desired operational motion and the ZMP trajectory. The feasibility of the proposed algorithms is verified by simulating and experimenting several motions though a planar 3-DOF manipulator model.

The paper proposes a model for the control of a multisegmented arm with redundant degrees of freedom. On the basis of an earlier model, the so-called MMC net, a simplified version is proposed here which has several advantages. First, it can easily be scaled up for the 3D case. Second, for the linear version a complete convergence proof is possible. Third, the properties of the earlier model are unchanged, namely versatile control of the redundant system, immediate change from direct kinematics to inverse kinematics or any mixed control task, as well as robustness against singularities.

The spherical robot XK-III, designed with redundant degrees of freedom, addresses the limitations of existing pendulum spherical robot structures by enhancing mobility and environmental adaptability. A nonlinear dynamic model is developed for XK-III’s new drive structure, along with a nonlinear disturbance observer (NDOB) to mitigate perturbations. Additionally, a Fuzzy PID controller (FPID) is implemented to further enhance XK-III’s environmental adaptability. Experimental results confirm the effectiveness of the new design, showing that XK-III equipped with FPID and NDOB outperforms traditional control systems in terms of anti-disturbance capabilities. This research provides valuable insights for the use of spherical robots in complex environments.