Smart meters have enabled collecting detailed energy usage data, which can facilitate more accurate forecasting at both the individual building and household levels. In this paper, we introduce a hybrid energy consumption forecasting framework that combines the strengths of Long Short-Term Memory (LSTM) neural networks with Prophet. By leveraging LSTM's ability to capture temporal dependencies and Prophet's strength in modeling seasonality and trends, our framework aims to identify complex consumption patterns that traditional methods might miss. We further enhance the prediction accuracy of our model by incorporating weather data and lag features. Validation across different socioeconomic household profiles shows that the hybrid model outperforms standalone LSTM, Prophet, and SARIMA models in high income households associated with more stable consumption behavior, while its accuracy declines relative to LSTM in profiles where usage is more reactive, usually found in lower socioeconomic segments. Overall, The work aims to improve energy management strategies, enabling more targeted and effective interventions in households where socio-economic factors influence consumption patterns.

This work presents an algorithm based on the Nondominated Sorting Genetic Algorithm II (NSGA-II) to solve multi-objective offline Multi-Robot Coverage Path Planning (MCPP) problems. The proposed algorithm embeds a donation-mutation operator and a multiple-parent crossover that generates solutions which maintain the longest path while minimizing the average path length. The algorithm also uses a library of elitism-selected high-fitness robot paths, and tournament-selected high min-max fitness paths, to construct high multi-objective fitness offspring. We evaluate the performance of our proposed algorithm against the state-of-the-art NSGA-II extended with an improved Heuristic Genetic Algorithm Crossover, and we demonstrate that for different instances of the MCPP problem, the Pareto-fronts of our proposed algorithm are not dominated by any of the points of the fronts generated by the state-of-the-art NSGA-II. A comparison has also been performed in a virtual environment simulating five drones inspecting three wind turbines. Results show that our approach exhibits a higher convergence rate for higher values of the ratio between the number of points to visit and the number of drones.

Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned, or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracing or using detailed a priori information of the network. In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields, and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios.

We present a method for estimating the contact event between sensor-free active subtracks, named flippers, of an articulated tracked vehicle (ATV) and the terrain surface. The main idea is to consider both the moving base link and unexpected collisions dynamics as disturbances of the flipper dynamics. On this basis we extend the generalized momenta fault detection and isolation (FDI) method to compute the residual dynamics of the flippers, without resorting to additional sensory information. Under the hypothesis that the residual signal presents disturbance patterns that can be discriminated by those generated by unexpected collisions of the flippers with the ground, we apply a classification method to recover the contact event. The wavelet packet transform is used to decompose the signal and to generate a feature space for the residual, from the different subbands. Finally, sparse SVM, based on feature selection discriminates the contact signal.

We present a real time method for updating a 3D map with dynamic obstacles detection. Moving obstacles are detected through ray-casting on spherical voxelization of point clouds. We evaluate the accuracy of this method on a point cloud dataset, suitably constructed for testing ray-surface intersection under relative motion conditions. Moreover, we show the benefits of the map updating in a real robot equipped with a rotating LIDAR system, navigating in real world scenarios, populated by moving people.

We propose in this article a new approach to robot cognitive control based on a stimulus-response framework that models both a robot’s stimuli and the robot’s decision to switch tasks in response to or inhibit the stimuli. In an autonomous system, we expect a robot to be able to deal with the whole system of stimuli and to use them to regulate its behavior in real-world applications. The proposed framework contributes to the state of the art of robot planning and high-level control in that it provides a novel perspective on the interaction between robot and environment. Our approach is inspired by Gibson’s constructive view of the concept of a stimulus and by the cognitive control paradigm of task switching. We model the robot’s response to a stimulus in three stages. We start by defining the stimuli as perceptual functions yielded by the active robot processes and learned via an informed logistic regression. Then we model the stimulus-response relationship by estimating a score matrix that leads to the selection of a single response task for each stimulus, basing the estimation on low-rank matrix factorization. The decision about switching takes into account both an interference cost and a reconfiguration cost. The interference cost weighs the effort of discontinuing the current robot mental state to switch to a new state, whereas the reconfiguration cost weighs the effort of activating the response task. A choice is finally made based on the payoff of switching. Because processes play such a crucial role both in the stimulus model and in the stimulus-response model, and because processes are activated by actions, we address also the process model, which is built on a theory of action. The framework is validated by several experiments that exploit a full implementation on an advanced robotic platform and is compared with two known approaches to replanning. Results demonstrate the practical value of the system in terms of robot autonomy, flexibility, and usability.

The paper presents a novel framework for 3D autonomous navigation for tracked vehicles. The framework takes care of clustering and segmentation of point clouds, traversability analysis, autonomous 3D path planning, motion planning and flippers control. Results illustrated in an experiment section show that the framework is promising to face harsh terrains. Robot performance is proved in three main experiments taken in a training rescue area, on fire escape stairs and in a non-planar testing environment, built ad-hoc to prove 3D path planning functionalities. Performance tests are also presented.

Modeling cognitive control is a major issue in robot control, and it is about deciding when a task cannot succeed and a new task need to be initiated. These decisions are induced by incoming stimuli alerting of events taking place while the robot is executing its duties. To learn cognitive control we address the human inspired mechanisms that govern cognitive control and that have been widely studied in neuroscience, namely, shifting and inhibition. Shifting and inhibition are, in fact, executive cognitive functions responding selectively to stimuli, so as to switch from one activity to a more compelling one or to inhibit inappropriate urges and preserve focus on the current task. In an autonomous system these cognitive skills are crucial to assess a well-regulated reactive behavior, which is of particular relevance in critical circumstances. In this paper we illustrate a new method developed for learning shifting and inhibition, based on Gaussian Processes, and using examples provided by skilled operators. We finally show that the learning method is promising and can be seen as a new view for modeling robot reactive and proactive behaviors.