Order Picking in warehouses is often optimized with a method known as Order Batching, which means thatone vehicle can be assigned to pick a batch of several orders at a time. Although there exists a rich body ofresearch on Order Batching Problem (OBP) optimization, one area which demands more attention is that ofcomputational efficiency, especially for optimization scenarios where warehouses have unconventionallayouts and vehicle capacity configurations. Due to the NP-hard nature of the OBP, computational cost foroptimally solving large instances is often prohibitive. In this paper we compare the performance of twoapproximate optimizers designed for maximum computational efficiency. The first optimizer, Single BatchIterated (SBI), is based on a Seed Algorithm, and the second, Metropolis Batch Sampling (MBS), is based ona Metropolis algorithm. Trade-offs in memory and CPU-usage and generalizability of both algorithms isanalysed and discussed. Existing benchmark datasets are used to evaluate the optimizers on various scenarios.On smaller instances we find that both optimizers come within a few percentage points of optimality atminimal CPU-time. For larger instances we find that solution improvement continues throughout the allottedtime but at a rate which is difficult to justify in many operational scenarios. SBI generally outperforms MBSand this is mainly attributed to the large search space and the latter’s failure to efficiently cover it. Therelevance of the results within Industry 4.0 era warehouse operations is discussed.

To make autonomous, affordable ships feasible in the real world, they must be capable of safely navigating without fully relying on GPS, high-resolution 3D maps, or high-performance navigation sensors. We suggest a method for estimating the position using affordable navigation sensors (compass and speed log or inertial navigation sensor), sensors used for perception of the environment (cameras, echo sounder, magnetometer), and publicly available maps (sea charts and magnetic intensity anomalies maps). A real-world field trial has shown that the proposed fusion mechanism provides accurate and robust navigation, applicable for affordable autonomous ships.

We compared three different Graphical User Interfaces (GUI) that we have designed and implemented to enable human supervision of an unmanned ship. Our findings indicate that a 3D GUI presented either on a screen or in a Virtual Reality (VR) setting provides several objective and subjective benefits compared to a Baseline GUI representing traditional tools.

We assume that an intuitive means for the specification, re-use, modification and transfer of synchronized motions-both regarding the two arms of a dual-arm robotic system, as well as regarding the coordination of a user and a robot-is key in interactive and collaborative settings as they are currently targeted for industrial applications. We show, how our knowledge based approach to end-user programming of synchronized motions and other generalizable, robot-agnostic skills can support such specification of coordinated actions between two robot arms and explain how that could be extended to include coordination with a human user. We describe the underlying ontologies and possibilities to populate those with an interface for intuitive programming, and show the generality of our approach through a task transfer between different kinematics (different robots), where the user is supported through underlying reasoning about the fulfillment of certain parameters or constraints for the involved skills.

The flexibility demands in manufacturing are severe, e.g., for rapid-change-over to new product variants, while robots are flexible machines that potentially can be adapted to a large variety of production tasks. Task definitions such as explicit robot programs are hardly reusable from an application point-of-view. To improve the situation, a knowledge-based approach exploiting distributed declarative information and cloud computing offers many possibilities for knowledge exchange and reuse, and it has the potential to facilitate new business models for industrial solutions. However, there are many unresolved questions yet, e.g., those related to reliability, consistency, or legal responsibility. To investigate some of these issues, different knowledge-based architectures have been prototyped and evaluated by confronting the solution candidates with key application demands. The conclusion is that distributed cloud-based approaches offer many possibilities, but there is still a need for further research and better infrastructure before this approach can become industrially attractive.

We study agents situated in partially observable environments, who do not have sufficient resources to create conformant plans. Instead, they generate plans which are conditional and partial, execute or simulate them, and learn to evaluate their quality from experience. Our agent employs an incomplete symbolic deduction system based on Active Logic and Situation Calculus for reasoning about actions and their consequences. An Inductive Logic Programming algorithm generalises observations and deduced knowledge, allowing the agent to execute a good plan. We show results of using PROGOL learning algorithm to distinguish "bad" plans early in the reasoning process, before too many resources are wasted on considering them. We show that additional knowledge needs to be provided before learning can be successful, but argue that the benefits achieved make it worthwhile. Finally, we identify several assumptions made by PROGOL, shared by other similarly universal algorithms, which are well justified in general, but fail to exploit the properties of the class of problems faced by rational agents.

The balance between exploration and exploitation is one of the key problems of action selection in Q-learning. Pure exploitation causes the agent to reach the locally optimal policies quickly, whereas excessive exploration degrades the performance of the Q-learning algorithm even if it may accelerate the learning process and allow avoiding the locally optimal policies. In this paper, finding the optimum policy in Q-learning is described as search for the optimum solution in combinatorial optimization. The Metropolis criterion of simulated annealing algorithm is introduced in order to balance exploration and exploitation of Q-learning, and the modified Q-learning algorithm based on this criterion, SA-Q-learning, is presented. Experiments show that SA-Q-learning converges more quickly than Q-learning or Boltzmann exploration, and that the search does not suffer of performance degradation due to excessive exploration.

The problem of finding appropriate probabilities for crossover and mutation with respect to resampling may be addressed using the Markov chain model. Our efforts in this direction lead through a simplification of the mixing matrix incorporating both probabilities. We present the simplification and discuss some of its ramifications. We expect that it may lead to some improvement of the computational properties of the Markov chain model of the simple genetic algorithm.