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The Conflict-Free Electric Vehicle Routing Problem (CF-EVRP) is an extension of the Vehicle Routing Problem (VRP), a combinatorial optimization problem of designing routes for vehicles to visit customers such that a cost function, typically the number of vehicles or the total travelled distance, is minimized. The problem finds many logistics applications, particularly for highly automated logistic systems for material handling. The CF-EVRP involves constraints such as time windows on the delivery to the customers, limited operating range of the vehicles, and limited capacity on the number of vehicles that a road segment can accommodate at the same time. In this paper, the compositional algorithm ComSat for solving the CF-EVRP is presented. The algorithm iterates through the sub-problems until a globally feasible solution is found. The proposed algorithm is implemented using an optimizing SMT-solver and is evaluated against an implementation of a previously presented monolithic model. The soundness and completeness of the algorithm are proven, and it is benchmarked on a set of generated problems and found to be able to solve problems of industrial size.

This paper presents an approach to collision-free, long-range trajectory generation for a mobile robot in an industrial environment with static and dynamic obstacles. For the long-range planning a visibility graph together with A* is used to find a collision-free path with respect to the static obstacles. This path is used as a reference path to the trajectory planning algorithm that in addition handles dynamic obstacles while complying with the robot dynamics and constraints. A Nonlinear Model Predictive Control (NMPC) solver generates a collision-free trajectory by staying close to the initial path but at the same time obeying all constraints. The NMPC problem is solved efficiently by leveraging the new numerical optimization method Proximal Averaged Newton for Optimal Control (PANOC). The algorithm was evaluated by simulation in various environments and successfully generated feasible trajectories spanning hundreds of meters in a tractable time frame.

With the clear aim of being on the forefront in the area of collaborative robots (cobots), the Volvo Group Truck Operations has created a collaboration arena where academia, industry, and start-up companies can share visions and needs and jointly create research and/or technology development projects. The arena has been active for about 5 years and is highly appreciated and beneficial for all involved parties. Advanced prototypes of robots and novel control methods for the usage of collaborative robots in an environment where human operators and robots are acting and collaborating on “equal terms” have been developed.

Automation and robotics are two enablers for developing the Smart Factory of the Future, which is based on intelligent machines and collaboration between robots and humans. Especially in final assembly and its material handling, where traditional automation is challenging to use, collaborative robot (cobot) systems may increase the flexibility needed in future production systems. A major obstacle to deploy a truly collaborative application is to design and implement a safe and efficient interaction between humans and robot systems while maintaining industrial requirements such as cost and productivity. Advanced and intelligent control strategies is the enabler when creating this safe, yet efficient, system, but is often hard to design and build.This paper highlights and discusses the challenges in meeting safety requirements according to current safety standards, starting with the mandatory risk assessment and then applying risk reduction measures, when transforming a typical manual final assembly station into an intelligent collaborative station. An important conclusion is that current safety standards and requirements must be updated and improved and the current collaborative modes defined by the standards community should be extended with a new mode, which in this paper is refereed to the deliberative planning and acting mode.

In many modern automation solutions, manual off-line programming is being replaced by online algorithms that dynamically perform tasks based on the state of the environment. Complexities of such systems are pushed even further with collaboration among robots and humans, where intelligent machines and learning algorithms are replacing more traditional automation solutions. This chapter describes the development of an industrial demonstrator using a control infrastructure called Sequence Planner (SP), and presents some lessons learned during development. SP is based on ROS2 and it is designed to aid in handling the increased complexity of these new systems using formal models and online planning algorithms to coordinate the actions of robots and other devices. During development, SP can auto generate ROS nodes and message types as well as support continuous validation and testing. SP is also designed with the aim to handle traditional challenges of automation software development such as safety, reliability and efficiency. In this chapter, it is argued that ROS2 together with SP could be an enabler of intelligent automation for the next industrial revolution.

During the last five decades, automation and robotics have transformed the automotive industry by increasing efficiency and improving the product quality. However, future trucks that will be autonomous, electrical and connected will require a completely new type of flexibility and intelligence in the production systems, especially in the final assembly. To handle the increased complexity of the products, production processes and logistic systems, final assembly must be transformed into collaborative and intelligent automation systems. These systems will include collaborative and deliberative robots (cobots), advanced vision-based control, adaptive safety systems, online optimization and learning algorithms and connected and well-informed human operators. But it will be a huge undertaking to transform current trucks industry such that they can design, implement and maintain large scale collaborative and intelligent automation systems. This paper presents the challenges with current planning and preparation processes for final assembly as well as the requirement and possible solutions for the future processes. An industrial use case at Volvo Trucks based on Sequence Planner and ROS2 is used to evaluate the proposed planning and preparation processes.