The coordination of cyclic multi-robot systems is a critical issue to avoid collisions but also to obtain the shortest cycle-time. This paper presents a novel methodology for trajectory and coordination optimisation of cyclic multi-robot systems. Both velocity tuning and time delays are used to coordinate the robots that operate in close proximity and avoid collisions. The novel element is the non-linear programming optimisation model that directly co-adjusts the multi-robot coordination during the trajectory optimisation, which allows optimising these as one problem. The methodology is demonstrated for productivity/smoothness optimisation, and for energy efficiency optimisation. An experimental validation is done for a real-world case study that considers the multi-robot material handling system of a multi-stage tandem press line. The results show that the productivity optimisation with the methodology is competitive compared to previous research and that substantial improvements can be achieved, e.g. up to 50% smoother trajectories and 14% reduction in energy consumption for the same productivity. This paper addresses the current lack of systematic methodologies for generating optimal coordinated trajectories for cyclic multi-robot systems to improve the productivity, smoothness, and energy efficiency.

This paper presents the Constructive Cooperative Coevolutionary ( C3C3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3C3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3C3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3C3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C3C3 is compared with several state-of-the-art algorithms, which shows that C3C3 is among the most competitive algorithms. C3C3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3C3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework.

This article investigates multi-objective optimization of the robot trajectories and position-based operation-coordination of complex multi-robot systems, such as press lines, to improve the production rate and obtaining smooth motions to avoid excessive wear of the robots’ components. Different functions for handling the multiple objectives are evaluated on realworld press lines, including both scalarizing single-objective functions and Pareto-based multi-objective functions. Additionally, the Multi-Objective Constructive Cooperative Coevolutionary (moC3) algorithm is proposed, for Pareto-based optimization, which uses a novel constructive initialization of the subpopulations in a co-adaptive fashion. It was found that Paretobased optimization performs better than the scalarizing single-objective functions. Furthermore, moC3 gives substantially better results compared to manual online tuning, as currently used in the industry. Optimizing robot trajectories and operation-coordination of complex multi-robot systems using the proposed method with moC3 significantly improves productivity and reduces maintenance. This article hereby addresses the lack of systematic methods for effectively improving the productivity of press lines.

The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DE's performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution (C3DE) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution (C3iDE), which removes several limitations with the previous version. A novel element of C3iDE is the advanced initialisation of the subpopulations. C3iDE initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of C3iDE on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with C3iDE is also investigated in this paper.

In this paper, a predicate transition model for discrete-event systems is generalized to include continuous dynamics, and the result is a modular hybrid predicate transition model. Based on this model, a hybrid Petri net including explicit differential equations and shared variables is also proposed. It is then shown how this hybrid Petri net model can be optimized based on a simple and robust nonlinear programming formulation. The procedure only assumes that desired sampled paths for a number of interacting moving devices are given, while originally equidistant time instances are adjusted to minimize a given criterion. This optimization of hybrid systems is also applied to a real robot station with interacting devices, which results in about 30% reduction in energy consumption. Moreover, a flexible online and event-based information architecture called the Tweeting Factory is proposed. Simple messages (tweets) from all kinds of equipment are combined into high-level knowledge, and it is demonstrated how this information architecture can be used to support optimization of robot stations.

Optimisation of the control function for multiple automated interacting production stations is a complex problem, even for skilled and experienced operators or process planners. When using mathematical optimisation techniques, it often becomes necessary to use simulation models to represent the problem because of the high complexity (i.e. simulation-based optimisation). Standard optimisation techniques are likely to either exceed the practical time frame or under-perform compared to the manual tuning by the operators or process planners. This paper presents the Constructive cooperative coevolutionary (C3) algorithm, which objective is to enable effective simulation-based optimisation for the control of automated interacting production stations within a practical time frame. C3 is inspired by an existing cooperative coevolutionary algorithm. Thereby, it embeds an algorithm that optimises subproblems separately. C3 also incorporates a novel constructive heuristic to find good initial solutions and thereby expedite the optimisation. In this work, two industrial optimisation problems, involving interaction production stations, with different sizes are used to evaluate C3. The results illustrate that with C3, it is possible to optimise these problems within a practical time frame and obtain a better solution compared to manual tuning.

In order to obtain high structural integrity and joint performance in welding a transient heat conduction model has been utilized to design a model based feedback controller.Gas tungsten arc welding of work-pieces of austenitic steel have been simulated by the finite element method. The basis for controller design is a low order model obtained from parametric system identification. The identification has been performed both on the finite element simulation and on physical welding. The low order model responses show a good agreement with both the finite element simulation result and the physical process response. An experimental study has been performed to verify the approach. This study also explores what experiments are needed for a successful design. It is shown that model based control successfully mitigates perturbations that occur during welding.

This paper presents successful feed forward control of additive manufacturing of fully dense metallic components. The study is a refinement of former control solutions of the process, providing more robust and industrially acceptable measurement techniques. The system uses a solid state laser that melts metal wire, which in turn is deposited and solidified to build the desired solid feature on a substrate. The process is inherently subjected to disturbances that might hinder consecutive layers to be deposited appropriately. The control action is a modified wire feed rate depending on the surface of the deposited former layer, in this case measured as a resistance. The resistance of the wire stick-out and the weld pool has shown to give an accurate measure of the process stability, and a solution is proposed on how to measure it. By controlling the wire feed rate based on the resistance measure, the next layer surface can be made more even. A second order iterative learning control algorithm is used for determining the wire feed rate, and the solution is implemented and validated in an industrial setting for building a single bead wall in titanium alloy. A comparison is made between a controlled and an uncontrolled situation when a relevant disturbance is introduced throughout all layers. The controller proves to successfully mitigate these disturbances and maintain stable deposition while the uncontrolled deposition fails.

Increased production rate and robot motion smoothness in a sheet metal press line are essential. Smooth robot motions avoid unplanned production interruptions and excessive wear of the robots. Reaching high production rate and smooth motions requires tuning of the tending press robot control to minimise the cycle time and jerk. Doing this for a press line with multiple robots is a complex large-scale problem. To model such problems for the optimisation process, computer simulations become almost essential. This work presents simulation-based optimisation of the time and jerk of robotic press tending operations and investigates the importance of including the robot motion’s smoothness. An optimiser works in concert with a simulation model of a sheet metal press line and its parametrised control system. The effect of including jerk minimisation in the objective function is tested on a real-world problem concerning a sheetmetal press line. The results illustrate the importance of including jerk-minimisation as an objective in the optimisation.Furthermore, the performance of this approach is compared with manual tuning by experienced operators. The results show that the proposed simulation-based optimisation approach outperforms manual tuning.

Simulation-based optimisation often considers computationally expensive problems. Successfully optimising such large scale and complex problems within a practical time frame is a challenging task. Optimisation techniques to fulfil this need to be developed. A technique to address this involves decomposing the considered problem into smaller subproblems. These subproblems are then optimised separately. In this paper, an efficient algorithm for simulation-based optimisation is proposed. The proposed algorithm extends the cooperative coevolutionary algorithm, which optimises subproblems separately. To optimise the subproblems, the proposed algorithm enables using a deterministic algorithm, next to stochastic genetic algorithms, getting the flexibility of using either type. It also includes a constructive heuristic that creates good initial feasible solutions to reduce the number of fitness calculations. The extension enables solving complex, computationally expensive problems efficiently. The proposed algorithm has been applied on automated sheet metal press lines from the automotive industry. This is a highly complex optimisation problem due to its non-linearity and high dimensionality. The optimisation problem is to find control parameters that maximises the line’s production rate. These control parameters determine velocities, time constants, and cam values for critical interactions between components. A simulation model is used for the fitness calculation during the optimisation. The results show that the proposed algorithm manages to solve the press line optimisation problem efficiently. This is a step forward in press line optimisation since this is to the authors’ knowledge the first time a press line has been optimised efficiently in this way.