Cooperative capture trajectory optimization of multi-space robots using an improved multi-objective fruit fly algorithm

In order to improve the operation efficiency and impact resistance of the robotic arm in unstructured environments such as orchards, this paper proposes an online trajectory planning method for multi-index comprehensive optimization. First, the kinematics and path planning model of the robotic arm is established; then, the joint motion trajectory is constructed using seven polynomials, and a multi-objective comprehensive trajectory optimization model is established using the nonlinear planning method based on kinematic constraints. Secondly, after obtaining the mathematical relationship of each optimization target through analysis, reconstruct the model, simplify the kinematic constraints by using the extreme value principle, and then define the target function by the weighting coefficient method to achieve the comprehensive optimization of the working time, energy consumption and acceleration of the robotic arm motion trajectory. Finally, use the algorithm to optimize the target function to obtain the comprehensive optimal motion trajectory, and take the citrus picking operation as an example for simulation and experimental testing. The simulation results show that the average calculation time of the algorithm is 61 ms, the time index of the obtained comprehensive optimal trajectory is 15.78 s, the energy consumption index is 188.86 °/s², the acceleration index is 405.08 °/s³, and the speed, acceleration and acceleration of each joint are all satisfied. Kinematic constraints. The experimental results show that the citrus picking robot arm runs smoothly and can meet the picking requirements. The average picking time for a single fruit is 16.4 s. This research provides a new method for the online trajectory planning of picking operations with high computational efficiency and can achieve multi-objective comprehensive optimization.

Hybrid multi-objective Harris Hawk optimization algorithm based on elite non-dominated sorting and grid index mechanism

In the model of hybrid flow shop scheduling problem with unrelated parallel machines, the makespan, total weighted earliness/tardiness and total waiting time are established as evaluation index. An algorithm of artificial bee colony based on the method of adaptive neighborhood search is designed. According to the characteristics of the model, initial processing sequence is used as solution vector in order to narrow down feasible solutions. Fitness of populations is distinguished by non-dominated sorting. In the process of iteration, excellent individuals are retained so that the diversity of population distribution is increased. Finally, the method is applied to a simulation example, compared with the traditional multi-objective algorithm. The results obtained demonstrate that the improved ABC algorithm for hybrid flow shop scheduling problem is good effective and diversified.

We investigate energy-optimal control of robots with ultracapacitor based regenerative drive systems. Based on a previously introduced framework, a fairly generic model is considered for the robot and the drive system. An optimal control problem is formulated to find point-to point trajectories maximizing the amount of energy regenerated and stored in the capacitor. The optimization problem, its numerical solution and an experimental evaluation are demonstrated using a PUMA 560 manipulator. A comprehensive experimental setup was prepared to evaluate power flows and energy regeneration. Tracking of optimal trajectories was enforced on the robot using a standard robust passivity based control approach. Experimental results show that when following optimal trajectories, a reduction of about 13\% in energy consumption can be achieved for the conditions of the study.

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.

In this article, we focus on safe and effective completion of a rendezvous and docking task by looking at planning approaches and control with fuel-optimal rendezvous for a target spacecraft running on a near-circular reference orbit. A variety of existent practical path constraints are considered, including the constraints of field of view, impulses, and passive safety. A rendezvous approach is calculated by using a hybrid genetic algorithm with those constraints. Furthermore, a control method of trajectory tracking is adopted to overcome the external disturbances. Based on Clohessy–Wiltshire equations, we first construct the mathematical model of optimal planning approaches of multiple impulses with path constraints. Second, we introduce the principle of hybrid genetic algorithm with both stronger global searching ability and local searching ability. We additionally explain the application of this algorithm in the problem of trajectory planning. Then, we give three-impulse simulation examples to acquire an optimal rendezvous trajectory with the path constraints presented in this article. The effectiveness and applicability of the tracking control method are verified with the optimal trajectory above as control objective through the numerical simulation.

Cloud Manufacturing (CMfg) utilizes the cloud computing paradigm to provide manufacturing services over the Internet flexibly and cost-effectively, where users only pay for what they use and may access services as needed. The scheduling method directly impacts the overall efficiency of CMfg systems. Manufacturing industries supply services aligned with customer-specific needs recorded in CMfg systems. CMfg managers develop manufacturing strategies based on real-time demand to establish service delivery timing. Many elements influence customer satisfaction, including dependability, timeliness, quality, and pricing. Therefore, CMfg depends on the use of multi-objective and real-time task scheduling. Multi-objective evolutionary algorithms have effectively examined many solutions, such as non-dominant, Pareto-efficient, and Pareto-optimal solutions, using both actual and synthetic workflows. This study introduces a new Multi-level Scheduling Model (MSM) and evaluates its effectiveness by comparing it with other multi-objective algorithms, including the weighted genetic algorithm, the non-dominated genetic sorting Algorithm II, and the starch Pareto evolution algorithm. The primary emphasis is on assessing the efficacy of algorithms and their suitability in commercial multi-cloud setups. The MSM's dynamic nature and adaptive features are emphasized, indicating its ability to effectively handle the complexity and demands of CMfg and resolve the scheduling issue within this environment. Experimental results suggest that MSM outperforms other algorithms by achieving a 20% improvement in makespan.

Hybrid flow shop scheduling (HFS) is an on sought problem modelling for production manufacturing. Due to its impact on productivity, researchers from different backgrounds have been attracted to solve its optimum solution. The HFS is a complex dilemma and provides ample solutions, thus inviting researchers to propose niche optimization methods for the problem. Recently, researchers have moved on to multi-objective solutions. In real-world situations, HFS is known for multi-objective problems, and consequently, the need for optimum solutions in multi-objective HFS is a necessity. Regarding sustainability topic, energy utilization is mainly considered as one of the objectives, including the common makespan criteria. This paper presents the existing multi-objective approach for solving energy utilization and makespan problems in HFS scheduling using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and a comparison to other optimization models was subjected for analysis. The model was compared with the most sought algorithm and latest multi-objective algorithms, Strength Pareto Evolutionary Algorithm 2 (SPEA -II), Multi-Objective Algorithm Particle Swarm Optimization (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The research interest starts with problem modelling, followed by a computational experiment with an existing multi-objective approach conducted using twelve HFS benchmark problems. Then, a case study problem is presented to assess all models. The numerical results showed that the NSGA-III obtained 50% best overall for distribution performance metrics and 42% best in convergence performance metrics for HFS benchmark problems. In addition, the case study results show that NSGA-III obtained the best overall convergence and distribution performance metrics. The results show that NSGA-III can search for the best fitness solution without compromising makespan and total energy utilization. In the future, these multi-objective algorithms’ potential can be further investigated for hybrid flow shop scheduling problems.

This paper presents a novel multi-objective Spiral Dynamic Optimization (MOSDA) algorithm. It is an extended version of a single objective type SDA. A Non-dominated sorting (NS) approach from Non-dominated Sorting Genetic Algorithm II (NSGAII) is adopted into SDA to develop its multi-objective (MO) type algorithm. SDA has a good elitism strategy and a simple structure. On the other hand, NS is a fast strategy to develop good Pareto Front (PF) characteristics for MO type algorithm. The proposed algorithm is tested with various benchmark functions used to test a newly developed MO algorithm. A PF graph is presented as a result of the test. Moreover, it is adopted to optimize parameters of Proportional-Derivative (PD) controller for an Inverted Pendulum (IP) system. Time-domain response of the IP is presented to show performance of the optimized controller. Result presented in this paper shows that MOSDA has a better performance in terms of finding PF and solution spread when tested with benchmark functions compared to NSGAII. In terms of its application in solving a real problem, both algorithms successfully optimize the PD and control the system very well. IP controlled by MOSDA-based PD shows better rise time.

This paper considers the problem of multi robot localization. The analysis is focused on the problem of determining which are the optimal robot trajectories in order to minimize the localization error at the final robot configurations. Furthermore, the analysis takes into account several constraints expressing the maximum robot energy consumption. The problem is formulated as an optimal control problem. For the special case of holonomic vehicles, an analytical solution is derived. In particular, this solution satisfies second order differential equations. The trajectories corresponding to this optimal control are plotted for different initial and final robot configurations and for several values of the system parameters (characterizing the precision of the robot sensors and the values of the constraints). Extensive simulations validate the derived analytical results.

In this paper, an L1 penalized sequential convex programming (LPSCP) method for trajectory optimization is proposed. Sequential convex methods dramatically reduce computation time for nonlinear trajectory optimizations, based on the desirable properties of convex optimization algorithms. Due to its fast convergence speed, the sequential convex method is considered as a near-future candidate for real-time optimal online guidance. However, the generic sequential convex method seldom suffers from poor robustness to crude the initial trajectory estimates and constraint perturbations, which limits its application to real-world systems. Suggested LPSCP method resolves the robustness issue of generic sequential convex methods, by addressing the convex subproblem infeasibility. While improving its robustness, proposed LPSCP method maintains its fast convergence property. Consequently, LPSCP method enhances overall versatility of sequential convex method for trajectory optimization, which includes ameliorated robustness toward initial trajectory estimates, and improved stability to state and constraint perturbations. Throughout the paper, details of LPSCP method are outlined along with an application example to missile trajectory optimization problems. Desirable properties of LPSCP method are demonstrated through several simulated examples. Optimized trajectories are compared with results from the pseudospectral method. Numerical simulation and Monte Carlo analysis show that LPSCP method accelerates optimization process dramatically (−98.2% on average) while producing the same optimal trajectory.

This work presents a new systematic solution to identify the vehicle inertia parameters which are essential inputs for vehicle simulation and vehicle safety research. In conceptual design phase of this work, a virtual three Degree-of-Freedom (DoF) test bench/ parallel manipulator (PM) whose moving platform is used to clamp vehicle under test is developed. In order to realize the kinematic characteristics of the proposed PM, the kinematic analysis consists of inverse kinematic and singularity architecture is carried out. Aiming at obtaining all ten vehicle inertia parameters (i.e., mass, center of gravity and inertia tensor), the observation matrix for parameter identification is derived from the dynamic model of PM. To get the dynamic model, the Euler’s equation and Lagrange approach are applied to implement the dynamic analysis for PM’s moving platform and actuators, respectively. It is beneficial to reduce the complexity of dynamic model and load of numerical computation. In the following section, to minimize the sensitivity of parameter identification to measurement noise, an optimization process of searching for the optimal movement trajectory of PM is proposed. For this purpose, the parameterized finite-Fourier-series are used to definite the general movement trajectory of PM firstly. Subsequently, the parameters of general trajectory are optimized by employing a nonlinear iterative algorithm. Objective of this algorithm is to obtain the minimal condition number of observation matrix and meanwhile to ensure the PM still works in the achievable working space during the test. The results show that the vehicle inertial parameters can be effectively identified by executing the single optimal movement trajectory on the PM. It is expected that the proposed systematic solution could be an important approach to improve the identification efficiency and identification accuracy of vehicle inertial parameters.

The substrate glass has the characteristics of large size, thin thickness, and easy breakage. The quality of the substrate glass handling robot directly affects the yield and production efficiency of flat panel display products. According to the characteristics of the substrate glass, the optimal design criteria of the robot end effector are proposed with a design of the optimal time and minimum impact multi-objective trajectory optimization scheme. By establishing a kinematic model, we analyze the forward kinematics and inverse kinematics of the robot and the singular configuration of the robot. Based on the multi-objective genetic algorithm, the Bspline curve is used to construct the robot trajectory, and its optimization model is established. MATLAB Robotics Tool carries out the motion simulation and trajectory planning example simulation of the MCL130 robot. The simulation shows that the motion time and joint impact of the robot have reached the optimum.

Pareto-based multi-objective optimization algorithms prefer non-dominated solutions over dominated solutions and maintain as much as possible diversity in the Pareto optimal set to represent the whole Pareto-front. This paper proposes three multi-objective Artificial Bee Colony (ABC) algorithms based on synchronous and asynchronous models using Pareto-dominance and non-dominated sorting: asynchronous multi-objective ABC using only Pareto-dominance rule (A-MOABC/PD), asynchronous multi-objective ABC using non-dominated sorting procedure (A-MOABC/NS) and synchronous multi-objective ABC using non-dominated sorting procedure (S-MOABC/NS). These algorithms were investigated in terms of the inverted generational distance, hypervolume and spread performance metrics, running time, approximation to whole Pareto-front and Pareto-solutions spaces. It was shown that S-MOABC/NS is more scalable and efficient compared to its asynchronous counterpart and more efficient and robust than A-MOABC/PD. An investigation on parameter sensitivity of S-MOABC/NS was presented to relate the behavior of the algorithm to the values of the control parameters. The results of S-MOABC/NS were compared to some state-of-the art algorithms. Results show that S-MOABC/NS can provide good approximations to well distributed and high quality non-dominated fronts and can be used as a promising alternative tool to solve multi-objective problems with the advantage of being simple and employing a few control parameters.

This paper proposes a new Multi-Objective Plasma Generation Optimization (MOPGO) algorithm, and its non-dominated sorting mechanism is investigated for numerous challenging real-world structural optimization design problems. The Plasma Generation Optimization (PGO) algorithm is a recently reported physics-based algorithm inspired by the generation process of plasma in which electron movement and its energy level are based on excitation modes, de-excitation, and ionization processes. As the search progresses, a better balance between exploration and exploitation has a more significant impact on the results; thus, the crowding distance feature is incorporated in the proposed MOPGO algorithm. Also, the proposed posteriori method exercises a non-dominated sorting strategy to preserve population diversity, which is a crucial problem in multi-objective meta-heuristic algorithms. In truss design problems, minimization of the truss's mass and maximization of nodal displacement are considered objective functions. In contrast, elemental stress and discrete cross-sectional areas are assumed to be behavior and side constraints, respectively. The usefulness of MOPGO to solve complex problems is validated by eight truss-bar design problems. The efficacy of MOPGO is evaluated based on ten performance metrics. The results demonstrate that the proposed MOPGO algorithm achieves the optimal solution with less computational complexity and has a better convergence, coverage, diversity, and spread. The Pareto fronts of MOPGO are compared and contrasted with multi-objective passing vehicle search algorithm, multi-objective slime mould algorithm, multi-objective symbiotic organisms search algorithm, and multi-objective ant lion optimization algorithm. This study will be further supported with external guidance at https://premkumarmanoharan.wixsite.com/mysite.