A ROS2 based communication architecture for control in collaborative and intelligent automation systems

Collaborative robots are becoming part of intelligent automation systems in modern industry. Development and control of such systems differs from traditional automation methods and consequently leads to new challenges. Thankfully, Robot Operating System (ROS) provides a communication platform and a vast variety of tools and utilities that can aid that development. However, it is hard to use ROS in large-scale automation systems due to communication issues in a distributed setup, hence the development of ROS2. In this paper, a ROS2 based communication architecture is presented together with an industrial use-case of a collaborative and intelligent automation system.Comment: 9 pages, 4 figures, 3 tables, to be published in the proceedings of 29th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2019), June 201

Energy reduction of stochastic time-constrained robot stations

This paper looks at the problem of reducing the energy use of robot movements in a robot station with stochastic execution times, while keeping the productivity of the station. The problem is formulated as a stochastic optimization problem, that constrains the makespan of the station to meet a deadline with a high probability. The energy use of the station is a function of the execution times of the robot operations, and the goal is to reduce this energy use by finding the optimal execution times and operation order. A theoretical motivation to why the stochastic variables in the problem, under some conditions, can be approximated as independent and normally distributed is presented, together with a derivation of the max function of stochastic variables. This allows the stochastic optimization problem to be approximated with a deterministic version, that can be solved with a commercial solver. The accuracy of the deterministic approximation is evaluated on multiple numerical examples, which show that the method successfully reduces the energy use, while the deadlines of the stations are met with high probabilities

Application of the sequence planner control framework to an intelligent automation system with a focus on error handling

Future automation systems are likely to include devices with a varying degree of autonomy, as well as advanced algorithms for perception and control. Human operators will be expected to work side by side with both collaborative robots performing assembly tasks and roaming robots that handle material transport. To maintain the flexibility provided by human operators when introducing such robots, these autonomous robots need to be intelligently coordinated, i.e., they need to be supported by an intelligent automation system. One challenge in developing intelligent automation systems is handling the large amount of possible error situations that can arise due to the volatile and sometimes unpredictable nature of the environment. Sequence Planner is a control framework that supports the development of intelligent automation systems. This paper describes Sequence Planner and tests its ability to handle errors that arise during execution of an intelligent automation system. An automation system, developed using Sequence Planner, is subjected to a number of scenarios where errors occur. The error scenarios and experimental results are presented along with a discussion of the experience gained in trying to achieve robust intelligent automation

Compact Representation of Time-Index Job Shop Problems Using a Bit-Vector Formulation

The Job Shop Scheduling Problem (JSP) is a combinatorial optimization problem where jobs visit single-capacity machines while minimizing a cost function, typically the makespan. The problem can be extended to fit typical industrial scenarios such as flexible assembly shop floors or for coordinating fleets of automated vehicles. General purpose optimizers can handle extended versions of the problem that typically arise in industrial problems. Mixed Integer Linear Programming (MILP) solvers and recently optimizing Satisfiability Modulo Theory (SMT) solvers can be used as general solvers for JSP problems. There exist different formulations of JSP problems, among them the time-index (TI) model. The TI offers the advantage of providing strong lower bounds, though its drawback is the model size.In this paper we present a new formulation of the TI model suitable for optimizing SMT-solvers that support bit-vector theories. The new formulation is significantly more compact than the standard TI formulation and is thus reducing one of the major issues with the TI model.We benchmark two different optimizing SMT solvers supporting bit-vector theories, comparing the standard formulation of the TI to the new formulation on a set of benchmark instances. The computational analysis shows that the new formulation outperforms the standard one, being up to twice faster and regardless of the solver employed; moreover the model generated with the new formulation is considerably smaller than with the standard formulation

Guard extraction for modeling and control of a collaborative assembly station

A transition system represented by guards and actions can be amended by new guards computed in order to satisfy some specification. If the transition system is the result of composing smaller state machines, guard extraction can be used to put the new guards onto the guards the original state machines. Planning and verification can then be performed directly on the system with additional guards. In this paper we discuss the benefits of applying guard extraction as part of the modeling work in a modular control architecture, where reusable resources are composed using specifications. We show with an example from the development of an industrial demonstrator that even if the specification language is limited to invariant propositions, in practice many common safety specifications can be expressed when combined with a notion of which transitions are allowed to be restricted

A framework concept for data visualization and structuring in a complex production process

This paper provides a concept study for a visual interface framework together with the software Sequence Planner for implementation on a complex industrial process for extracting process information in an efficient way and how to make use of a lot of data to visualize it in a standardized human machine interface for different user perspectives. The concept is tested and validated on a smaller simulation of a paint booth with several interconnected and supporting control systems to prove the functionality and usefulness in this kind of production system.The paper presents the resulting five abstraction levels in the framework concept, from a production top view down to the signal exchange between the different resources in one production cell, together with additional features. The simulation proves the setup with Sequence Planner and the visual interface to work by extract and present process data from a running sequence

Reduced-order synthesis of operation sequences

In flexible manufacturing systems a large number of operations need to be coordinated and supervised to avoid blocking and deadlock situations. The synthesis of such supervisors soon becomes unmanageable for industrial manufacturing systems, due to state space explosion. In this paper we therefore develop some reduction principles for a recently presented model based on self-contained operations and sequences of operations. First sequential operation behaviors are identified and related operation models are simplified into one model. Then local transitions without interaction with other operation models are removed. This reduction principle is applied to a synthesis of non-blocking operation sequences, where collisions among moving devices are guaranteed to be avoided by a flexible booking process. The number of states in the synthesis procedure and the computation time is reduced dramatically by the suggested reduction principle

Towards compositional automated planning

The development of efficient propositional satisfiability problem solving algorithms (SAT solvers) in the past two decades has made automated planning using SAT-solvers\ua0an established AI planning approach. Modern SAT solvers can\ua0accommodate a wide variety of planning problems with a large number of variables. However, fast computing of reasonably long\ua0plans proves challenging for planning as satisfiability. In order to address this challenge, we present a compositional approach based on abstraction refinement that iteratively generates, solves and composes partial solutions from a parameterized planning problem. We show that this approach decomposes the monolithic planning problem into smaller problems and thus significantly speeds up plan calculation, at least for a class of tested planning problems

Evaluation of high level methods for efficient planning as satisfiability

Fast planning algorithms play a key role in intelligent automation systems where control sequences are constantly calculated. In order to determine which algorithms increase planning performance, we evaluate and compare several high level planning methods on a set of standard benchmarks. We focus on planning as satisfiability as the leading approach for solving difficult planning problems