自主智能系统(英文)最新文献

Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management (PHM). Indeed, PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour. To be effective in developing PHM programs, the most critical assets should be identified so to direct modelling efforts. Several techniques could be adopted to evaluate asset criticality; in industrial practice, criticality analysis is amongst the most utilised. Despite the advancement of artificial intelligence for data analysis and predictions, the criticality analysis, which is built upon both quantitative and qualitative data, has not been improved accordingly. It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i) fix the semantics behind the terms involved in the analysis, ii) standardize and uniform the way criticality analysis is performed, and iii) take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy. The developed ontology, called MOCA, is tested in a food company featuring a global footprint. The application shows that MOCA can accomplish the prefixed goals; specifically, high priority assets towards which direct PHM programs are identified. In the long run, ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way. As such, they will enable advanced analytics to take place, allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide.

To improve transportation capacity, dual overhead crane systems (DOCSs) are playing an increasingly important role in the transportation of large/heavy cargos and containers. Unfortunately, when trying to deal with the control problem, current methods fail to fully consider such factors as external disturbances, input dead zones, parameter uncertainties, and other unmodeled dynamics that DOCSs usually suffer from. As a result, dramatic degradation is caused in the control performance, which badly hinders the practical applications of DOCSs. Motivated by this fact, this paper designs a neural network-based adaptive sliding mode control (SMC) method for DOCS to solve the aforementioned issues, which achieves satisfactory control performance for both actuated and underactuated state variables, even in the presence of matched and mismatched disturbances. The asymptotic stability of the desired equilibrium point is proved with rigorous Lyapunov-based analysis. Finally, extensive hardware experimental results are collected to verify the efficiency and robustness of the proposed method.

We study the Transport and Pick Robots Task Scheduling (TPS) problem, in which two teams of specialized robots, transport robots and pick robots, collaborate to execute multi-station order fulfillment tasks in logistic environments. The objective is to plan a collective time-extended task schedule with the minimization of makespan. However, for this recently formulated problem, it is still unclear how to obtain satisfying results efficiently. In this research, we design several constructive heuristics to solve this problem based on the introduced sequence models. Theoretically, we give time complexity analysis or feasibility guarantees of these heuristics; empirically, we evaluate the makespan performance criteria and computation time on designed dataset. Computational results demonstrate that coupled append heuristic works better for the most cases within reasonable computation time. Coupled heuristics work better than decoupled heuristics prominently on instances with relative few pick robot numbers and large work zones. The law of diminishing marginal utility is also observed concerning the overall system performance and different transport-pick robot numbers.

In the context of collaborative robotics, distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans. We describe this as the creation of Hastily Formed Knowledge Networks (HFKNs). The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans. The information collected ranges from low-level sensor data to high-level semantic knowledge, the latter represented in part as RDF Graphs. The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents. This is done through the distributed synchronization of RDF Graphs shared between agents. High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members. The system is empirically validated and complexity results of the proposed algorithms are provided. Additionally, a field robotics case study is described, where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.

Unmanned aerial vehicles (UAVs) have been found significantly important in the air combats, where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and dynamics. The key to empower the UAVs with such capability is the autonomous maneuver decision making. In this paper, an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed. First, based on the process of air combat and the constraints of the swarm, the motion model of UAV and the multi-to-one air combat model are established. Second, a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation. Then, a swarm air combat algorithm based on deep deterministic policy gradient strategy (DDPG) is proposed for online strategy training. Finally, the effectiveness of the proposed algorithm is validated by multi-scene simulations. The results show that the algorithm is suitable for UAV swarms of different scales.

This paper considers the scenario where multiple robots collaboratively cover a region in which the exact distribution of workload is unknown prior to the operation. The workload distribution is not uniform in the region, meaning that the time required to cover a unit area varies at different locations of the region. In our approach, we divide the target region into multiple horizontal stripes, and the robots sweep the current stripe while partitioning the next stripe concurrently. We propose a distributed workload partition algorithm and prove that the operation time on each stripe converges to the minimum under the discrete-time update law. We conduct comprehensive simulation studies and compare our method with the existing methods to verify the theoretical results and the advantage of the proposed method. Flight experiments on mini drones are also conducted to demonstrate the practicality of the proposed algorithm.

The focus of this paper is on base functionalities required for UAV-based rapid deployment of an ad hoc communication infrastructure in the initial phases of rescue operations. The main idea is to use heterogeneous teams of UAVs to deploy communication kits that include routers, and are used in the generation of ad hoc Wireless Mesh Networks (WMN). Several fundamental problems are considered and algorithms are proposed to solve these problems. The Router Node Placement problem (RNP) and a generalization of it that takes into account additional constraints arising in actual field usage is considered first. The RNP problem tries to determine how to optimally place routers in a WMN. A new algorithm, the RRT-WMN algorithm, is proposed to solve this problem. It is based in part on a novel use of the Rapidly Exploring Random Trees (RRT) algorithm used in motion planning. A comparative empirical evaluation between the RRT-WMN algorithm and existing techniques such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Particle Swarm Optimization (PSO), shows that the RRT-WMN algorithm has far better performance both in amount of time taken and regional coverage as the generalized RNP problem scales to realistic scenarios. The Gateway Node Placement Problem (GNP) tries to determine how to locate a minimal number of gateway nodes in a WMN backbone network while satisfying a number of Quality of Service (QoS) constraints.Two alternatives are proposed for solving the combined RNP-GNP problem. The first approach combines the RRT-WMN algorithm with a preexisting graph clustering algorithm. The second approach, WMNbyAreaDecomposition, proposes a novel divide-and-conquer algorithm that recursively partitions a target deployment area into a set of disjoint regions, thus creating a number of simpler RNP problems that are then solved concurrently. Both algorithms are evaluated on real-world GIS models of different size and complexity. WMNbyAreaDecomposition is shown to outperform existing algorithms using 73% to 92% fewer router nodes while at the same time satisfying all QoS requirements.

In this paper, aiming at the flying scene of the small unmanned aerial vehicle (UAV) in the low-altitude suburban environment, we choose the sensor configuration scheme of LiDAR and visible light camera, and design the static and dynamic obstacle detection algorithms based on sensor fusion. For static obstacles such as power lines and buildings in the low-altitude environment, the way that image-assisted verification of point clouds is used to fuse the contour information of the images and the depth information of the point clouds to obtain the location and size of static obstacles. For unknown dynamic obstacles such as rotary-wing UAVs, the IMM-UKF algorithm is designed to fuse the distance measurement information of point clouds and the high precision angle measurement information of image to achieve accurate estimation of the location and velocity of the dynamic obstacles. We build an experimental platform to verify the effectiveness of the obstacle detection algorithm in actual scenes and evaluate the relevant performance indexes.

Autonomous vehicles have been envisioned for more than 100 years. One of the first suggestions was a front cover of Scientific America back in 1916. Today, it is possible to get cars that drive autonomously for extended distances. We are also starting to see micro-mobility solutions, such as the Nuro vehicles for pizza delivery. Building autonomous cars that can operate in urban environments with a diverse set of road-users is far from trivial. Early 2018 the Contextual Robotics Institute at UC San Diego launched an effort to build a full stack autonomous vehicle for micro-mobility. The motivations were diverse: i) development of a system for operation in an environment with many pedestrians, ii) design of a system that does not rely on dense maps (or HD-maps as they are sometimes named), iii) design strategies to build truly robust systems, and iv) a framework to educate next-generation engineers. In this paper, we present the research effort of design, prototyping, and evaluation of such a vehicle. From the evaluation, several research directions are explored to account for shortcomings. Lessons and issues for future work are additionally drawn from this work.

A research arena (WARA-PS) for sensing, data fusion, user interaction, planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented. The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges. The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration. This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles. The motivating application for the demonstration is marine search and rescue operations. A state-of-art delegation framework for the mission planning together with three specific applications is also presented. The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles. The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles, and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments. The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility. It would be most difficult to do experiments on this large scale without the WARA-PS research arena. Furthermore, these demonstrator activities have resulted in effective research dissemination with high public visibility, business impact and new research collaborations between academia and industry.