The integration of unmanned aerial vehicles (UAVs) into cellular networks presents significant mobility management challenges, primarily due to frequent handovers caused by probabilistic line-of-sight conditions with multiple ground base stations (BSs). To tackle these challenges, reinforcement learning (RL)-based methods, particularly deep Q-networks (DQN), have been employed to optimize handover decisions dynamically. However, a major drawback of these learning-based approaches is their black box nature, which limits interpretability in the decision making process. This paper introduces an explainable AI (XAI) framework that incorporates Shapley Additive Explanations (SHAP) to provide deeper insights into how various state parameters influence handover decisions in a DQN-based mobility management system. By quantifying the impact of key features such as reference signal received power (RSRP), reference signal received quality (RSRQ), buffer status, and UAV position, our approach enhances the interpretability and reliability of RL-based handover solutions. To validate and compare our framework, we utilize real-world network performance data collected from UAV flight trials. Simulation results show that our method provides intuitive explanations for policy decisions, effectively bridging the gap between AI-driven models and human decision-makers.

Many recent works have proposed algorithms for information gathering that benefit from multi-robot cooperation. However, most algorithms either employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics; or cannot deal with inter-robot restrictions like e.g. communication constraints. This paper presents an approach for multi-robot information gathering that tackles the two aforementioned issues. To this end we propose an algorithm that combines in an innovative manner Gaussian processes (GPs) to model the physical process of interest, RRT planners to plan paths in a continuous domain, and a distributed decision-making algorithm to achieve multi-robot cooperation. Specifically, we employ the Max-sum algorithm for distributed multi-robot cooperation by defining an information-theoretic utility function together with a path clustering approach. This function maximizes information gathering, subject to inter-robot communication constraints. We validate the proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness of the approach.

Information gathering algorithms aim to intelligently select the robot actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, or a magnetic field. Many recent works have proposed algorithms for information gathering. However, these algorithms employ discretization of the state space, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, most algorithms are not suited for online information gathering tasks. This paper presents a novel approach that tackles the two aforementioned issues. Specifically, our approach includes two intertwined steps: a Gaussian processes (GPs)-based prediction that allows a robot to identify highly unexplored locations, and an RRT*-based informative path planning that guides the robot towards those locations. The combination of the two steps allows an online realization of the algorithm, while eliminates the need of discretization. We demonstrate the effectiveness of the proposed algorithm in simulations, as well as with an experiment in which a ground-based robot explores the magnetic field intensity within an indoor environment populated with obstacles.

Exploration is a crucial problem in safety of life applications, such as search and rescue missions. Gaussian processes constitute an interesting underlying data model that leverages the spatial correlations of the process to be explored to reduce the required sampling of data. Furthermore, multi-agent approaches offer well known advantages for exploration. Previous decentralized multi-agent exploration algorithms that use Gaussian processes as underlying data model, have only been validated through simulations. However, the implementation of an exploration algorithm brings difficulties that were not tackle yet. In this work, we propose an exploration algorithm that deals with the following challenges: (i) which information to transmit to achieve multi-agent coordination; (ii) how to implement a light-weight collision avoidance; (iii) how to learn the data's model without prior information. We validate our algorithm with two experiments employing real robots. First, we explore the magnetic field intensity with a ground-based robot. Second, two quadcopters equipped with an ultrasound sensor explore a terrain profile. We show that our algorithm outperforms a meander and a random trajectory, as well as we are able to learn the data's model online while exploring.

We propose a novel algorithm for efficient exploration with a single agent in unknown environments, populated with static obstacles. Every next position is computed by employing environment inference on top of path planning. The path planning process that ensures obstacle avoidance uses a modified version of the A* algorithm [1] and the inference is performed by direct sensing and by predicting values at yet-not-visited positions using Gaussian processes. We have validated our algorithm with densely measured data of an indoor magnetic field with significant spatial variations in different obstacle setups. Additionally, it is shown that it outperforms a previously developed algorithm [2], for obstacle-free scenarios, and the random movement of the agent.