1,721,030 research outputs found
Reconstruction and prediction of the layout of indoor environments from two-dimensional metric maps
Full text linkMetric maps, like occupancy grids, are one of the most common ways to represent indoor environments in autonomous mobile robotics. Although they are effective for navigation and localization, metric maps contain little knowledge about the structure of the buildings they represent. In this paper, we propose a method that identifies the structure of indoor environments from 2D metric maps by retrieving their layout, namely an abstract geometrical representation that models walls as line segments and rooms as polygons. The method works by finding regularities within a building, abstracting from the possibly noisy information of the metric map, and uses such knowledge to reconstruct the layout of the observed part and to predict a possible layout of the partially observed portion of the building. Thus, differently of other methods from the state of the art, our method can be applied both to fully observed environments and, most significantly, to partially observed ones. Experimental results show that our approach performs effectively and robustly on different types of input metric maps and that the predicted layout is increasingly more accurate when the input metric map is increasingly more complete. The layout returned by our method can be exploited in several tasks, such as semantic mapping, place categorization, path planning, human–robot communication, and task allocation
Exploration of indoor environments through predicting the layout of partially observed rooms
No full textWe consider exploration tasks in which an autonomous mobile robot incrementally builds maps of initially unknown indoor environments. In such tasks, the robot makes a sequence of decisions on where to move next that, usually, are based on knowledge about the observed parts of the environment. In this paper, we present an approach that exploits a prediction of the geometric structure of the unknown parts of an environment to improve exploration performance. In particular, we leverage an existing method that reconstructs the layout of an environment starting from a partial grid map and that predicts the shape of partially observed rooms on the basis of geometric features representing the regularities of the indoor environment. Then, we originally employ the predicted layout to estimate the amount of new area the robot would observe from candidate locations in order to inform the selection of the next best location and to early stop the exploration when no further relevant area is expected to be discovered. Experimental activities show that our approach is able to exploit the predicted layout of partially observed rooms in order to speed up the exploration
Multi-agent path finding in configurable environments
No full textMulti-Agent Path Finding (MAPF) plays an important role in many real-life applications where autonomous agents must coordinate to reach their goals without collisions. MAPF problems often take place in structured environments that are usually assumed to be static and known in advance. In this paper, we introduce C-MAPF, i.e., MAPF in Configurable environments, a novel variant of the MAPF problem in which the environment is configurable, namely its structure and topology can be controlled within some given constraints. Consider, for instance, a warehouse logistics application: the environment can be changed (at least to some degree) by the managers of the warehouse, for example by re-arranging the positions of the shelves or by removing or adding temporary walls. We study the properties of the C-MAPF problem and we devise two algorithms for solving it, both based on Conflict-Based Search (CBS), a state-of-the-art MAPF algorithm. First, we present Parallel CBS (P-CBS), that searches for a solution by simultaneously considering all the possible configurations of the environment. We then present Abstract CBS (A-CBS), an extended version of the CBS algorithm that solves C-MAPF problems by introducing a new type of conflict on the allowable configurations of the environment. We prove that our solvers are both complete and optimal and we experimentally assess their performance in different settings
Preventing Deadlocks for Multi-Agent Pickup and Delivery in Dynamic Environments
No full textThe Multi-Agent Pickup and Delivery (MAPD) problem, in which a team of agents has to plan paths to accomplish incoming pickup and delivery tasks without collisions, has recently attracted significant attention both from academia and industry. In this paper, we consider a MAPD setting in which the environment is dynamic, namely it is populated by other moving agents, beyond those belonging to the team. For instance, in a warehouse, moving agents could be humans or cleaning robots. We assume that the team agents cannot communicate with the moving agents and cannot interfere with their tasks and paths, which are a priori unknown and cannot be modified. As a consequence, team agents have to reactively try to solve potential collisions when they appear. However, it can happen that some conflicts are not solvable without affecting the moving agents, resulting in deadlocks. Since deadlocks can become rather frequent, especially in crowded environments, in this paper we propose an approach that, by imposing minor constraints on the environment and the movements of the agents, solves potential collisions and prevents the formation of deadlocks by design. Experimental results show that our approach prevents deadlocks, even in very crowded environments, with negligible impact on the performance of task completion
Sophie Calle: tra fotografia e parola
No full textIl saggio analizza l'opera dell'artista francese Sophie Calle, opera nella quale si stabilisce una tensione continua tra immagine fotografica e linguaggio verbale, tensione che si risolve ogni volta con esiti imprevedibili e problematici
A Transfer Learning Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries
No full text—Industry 4.0 has reimagined how businesses manufacture and distribute their products. To advance further towards
sustainability, the field of Fault Diagnosis and Prognosis (FDP)
assumes great significance. The ability to predict failures or to
know when a component will reach the end of its operational
life can significantly mitigate maintenance and replacement costs.
Machine Learning (ML) methods have exhibited the ability to
extract trends and models from complex datasets, becoming wellsuited for FDP tasks. When dealing with FDP, one of the main
problems is the difficulty of obtaining large datasets, due to
the burden of conducting extensive laboratory tests, and their
usual unbalance, for the impossibility of simulating every possible
anomaly that could ever happen. Being able to generate new
synthetic data, or to adapt a pre-trained model to another similar
task, becomes of paramount importance. In this paper, we focus
on lithium-ion batteries. Several commonly used devices are
usually powered using lithium-ion batteries, and each of these
batteries can assume a completely different behavior from its
peers based on usage, charging, and many other factors, leading
to potential harm, unreliableness, and other major potential
issues. We propose a convolutional Long Short-Term Memory
(LSTM) neural network with attention for estimating Remaining
Useful Life (RUL) of lithium-ion batteries. The model will be
trained on a source dataset, and then re-trained on a smaller
target dataset to establish the possibility of applying domain
adaptation and transfer learning to RUL estimation, allowing
for fast deployment and cost reduction in the production phase.
Results show that the use of transfer learning helps to increase
the performance of the model, obtaining on the target dataset an
accuracy similar to that of the source dataset
Robot exploration of indoor environments using incomplete and inaccurate prior knowledge
Full text linkExploration is a task in which autonomous mobile robots incrementally discover features of interest in initially unknown environments. We consider the problem of exploration for map building, in which a robot explores an indoor environment in order to build a metric map. Most of the current exploration strategies used to select the next best locations to visit ignore prior knowledge about the environments to explore that, in some practical cases, could be available. In this paper, we present an exploration strategy that evaluates the amount of new areas that can be perceived from a location according to a priori knowledge about the structure of the indoor environment being explored, like the floor plan or the contour of external walls. Although this knowledge can be incomplete and inaccurate (e.g., a floor plan typically does not represent furniture and objects and consequently may not fully mirror the structure of the real environment), we experimentally show, both in simulation and with real robots, that employing prior knowledge improves the exploration performance in a wide range of settings
A journey among pairs of vertices: Computing Robots' paths for performing joint measurements
No full textThe problem of performing joint measurements recurs in many robotic applications, like constructing communication maps from signal strength samples gathered on the field. In spite of this, a theo ry supporting efficient algorithms has not been yet developed and ad hoc methods are usually employed. In this paper. we consider an environment represented by a metric graph and prove that the problem of Jointly performing measurements from given vertices is NP-hard when either the total traveled distance or the task comp letion time have to be minimized. Given the difficulty of finding optimal paths in an efficient way, we propose a greedy randomized approach able to cope with both the optimization objectives. In settings for which joint measurements must be taken for all pairs of vertices, we prove that a deterministic greedy algorithm achieves an O(m log n) approximation factor for the traveled distance object ive, where m is the number of robots and n the number of vertices, and an O(m2 log n) approximation factor for the completion time. Experiments in simulation show that our algorithms perform well in practice, also when compared to an ad hoc method taken from the literature
A search-based approach to solve pursuit-evasion games with limited visibility in polygonal environments
No full textA pursuit-evasion game is a non-cooperative game in which a pursuer tries to detect or capture an adversarial evader. We study a pursuit-evasion game which takes place in a known polygonal environment. The goal of the pursuer is to capture the evader by moving onto its location. The players can observe each others' locations only if they can "see" each other - i.e., if the line segment connecting their locations lies entirely inside the polygonal environment. The complexity of representing the information available to the players at a given time makes solving pursuit-evasion games with visibility limitations difficult. We represent the state of the game using an efficient visibility-based decomposition of the environ-. ment paired with a more classical grid-based decomposition. The optimal players' strategies are computed using a min-max search algorithm improved with specific speedup techniques that preserve optimality. We show that our decomposition is complete for a rash evader, which hides from the pursuer and does not move from its hiding location when the pursuer is not visible. Simulations in realistic indoor environments and comparison with a Monte Carlo tree search algorithm validate our approach
Mapping beyond what you can see: Predicting the layout of rooms behind closed doors
Full text linkThe availability of maps of indoor environments is often fundamental for autonomous mobile robots to efficiently operate in industrial, office, and domestic applications. When robots build such maps, some areas of interest could be inaccessible, for instance, due to closed doors. As a consequence, these areas are not represented in the maps, possibly causing limitations in robot localization and navigation. In this paper, we provide a method that completes 2D grid maps by adding the predicted layout of the rooms behind closed doors. The main idea of our approach is to exploit the underlying geometrical structure of indoor environments to estimate the shape of unobserved rooms. Results show that our method is accurate in completing maps also when large portions of environments cannot be accessed by the robot during map building. We experimentally validate the quality of the completed maps by using them to perform path planning tasks.(c) 2022 Elsevier B.V. All rights reserved
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