In this paper, we propose a robot oriented knowledge representation system based on the use of the Prolog language. Our framework hinges on a special organisation of Knowledge Base (KB) that enables: 1) its efficient population from natural language texts using semi-automated procedures based on Large Language Models (LLMs); 2) the seamless generation of temporal parallel plans for multi-robot systems through a sequence of transformations; 3) the automated translation of the plan into an executable formalism. The framework is supported by a set of open source tools and its functionality is shown with a realistic application.

Automation in industries is becoming an ever-increasing necessity, especially in the sector of logistics. In many cases, this means having many different automated guided vehicles (AGVs) moving at the same time, hence needing coordination to avoid conflicts between different agents. The problem of organizing a fleet of autonomous robots is known as the Multi-Agent Path Finding (MAPF) problem in the literature for which several optimal and sub-optimal algorithms have been proposed. When faced with real-life scenarios, these algorithms must provide the best feasible solution in the shortest time possible, therefore they must scale for large scenarios and be efficient. In this work, we briefly describe our open-source framework we are working on and we lay down the research paths we are going to focus on. The goal is to develop a holistic system that allows to control different aspects of the MAPF problem, from graph topology to goal scheduling.

This paper presents a parallel computing implementation of the Iterative Dynamic Programming (IDP) solution to the multipoint Markov-Dubins problem using GPUs. The multi-point Markov-Dubins problem requires the computation of the shortest path with bounded curvature that connects a sequence of planar points (waypoints). As well as being interesting in its own right, an efficient solution to this problem is key to finding optimal or suboptimal solutions of other problems such as the Dubins Travelling Salesman and the Dubins Orienteering problem. The constraint on the curvature makes the problem highly non-linear and complicates its solution. Classic methods are optimisation-based and cast the problem into the Nonlinear Programming (NLP) or Mixed Integer Nonlinear Programming (MINLP) frameworks, for which existing solutions cannot be significantly parallelised. On the contrary, the IDP solution proposed here is well suited for parallel execution. In the paper, we show that the parallel implementation of the IDP outperforms both the NLP/MINLP methods and the iterative version of the IDP methods in terms of accuracy, computation time and power consumption. Computation time and power consumption will be the main focus of the paper, because they are closely related to the implementation on an embedded platform.