In this paper, we investigate multi-objective opti-mization of roadmaps for multi-robot path planning. We propose a new representation for roadmaps based on polygons and explore its potentials on various scenarios. In addition, we define three objective functions to estimate the suitability of each roadmap for navigation, and propose a modification of the well-known NSGA-II algorithm to optimize the roadmaps. In our experiments, we compare the quality of the proposed optimized roadmaps with those based on regular grids. The results show that in complex environments with obstacles, the optimized roadmaps perform much more efficient than those on regular grids. In addition, the performance of the optimization can be significantly improved by using the regular grids to initialize the optimization process.

Implementing and conducting reproducible experiments on multi-robot hardware platforms are challenging tasks due to variations in hardware, software, and most importantly the intensive implementation effort. In this paper, we aim to present the Driving Swarm software framework which is developed to facilitate the implementation, deployment, supervision, and analysis of multi-robot experiments. We use this framework with the TurtleBot3 hardware platform and measure its performance for two example scenarios: trajectory tracking and flocking behavior, with 5 and 6 robots. The goal of our experiments is to validate the simulation comparing to hardware implementations and to provide a baseline data for further experiments. While the simulated and real robots show similar behavior, we could observe that the simulated behavior is more robust than the real behavior in both scenarios. This effect is observed in the lower tracking error and better obstacle avoidance in both experiments. While the simulation proved to be a valuable tool during the development of the behaviors, the results confirm the importance of conducting experiments on a real-world test bed.

The Multi-Agent Pathfinding problem (MAPF) has several applications in industry and robotics. The aim of a MAPF-solver is to find a set of optimal and non-overlapping paths for a number of agents in a navigation scenario. Existing approaches are shown to successfully deal with MAPF, where either the makespan or flow-time is used as a single objective. In this article, we treat the MAPF as a multi-objective optimisation problem (MOMAPF). In this paper, we consider three different objective functions, called makespan, flow-time and path-overlaps which are to be optimised at the same time. The MOMAPF problem in this paper is designed to be a scalable test problem for multi-objective optimisation algorithms, where we can scale up the variable space to reflect different real-world scenarios. We propose a new problem formulation for MOMAPF optimisation algorithms and implement it into the NSGA-II and NSGA-III and provide an experimental evaluation of the optimisation results.