Job Selection in a Network of Autonomous UAVs for Delivery of Goods

This article analyzes two classes of job selection policies that control how a network of autonomous aerial vehicles delivers goods from depots to customers. Customer requests (jobs) occur according to a spatio-temporal stochastic process not known by the system. If job selection uses a policy in which the first job (FJ) is served first, the system may collapse to instability by removing just one vehicle. Policies that serve the nearest job (NJ) first show such threshold behavior only in some settings and can be implemented in a distributed manner. The timing of job selection has significant impact on delivery time and stability for NJ while it has no impact for FJ. Based on these findings we introduce a methodological approach for decision-making support to set up and operate such a system, taking into account the trade-off between monetary cost and service quality. In particular, we compute a lower bound for the infrastructure expenditure required to achieve a certain expected delivery time. The approach includes three time horizons: long-term decisions on the number of depots to deploy in the service area, mid-term decisions on the number of vehicles to use, and short-term decisions on the policy to operate the vehicles

Job selection in a network of autonomous UAVs for delivery of goods

This article analyzes two classes of job selection policies that control how a network of autonomous aerial vehicles delivers goods from depots to customers. Customer requests (jobs) occur according to a spatio-temporal stochastic process not known by the system. If job selection uses a policy in which the first job (FJ) is served first, the system may collapse to instability by removing just one vehicle. Policies that serve the nearest job (NJ) first show such threshold behavior only in some settings and can be implemented in a distributed manner. The timing of job selection has significant impact on delivery time and stability for NJ while it has no impact for FJ. Based on these findings we introduce a methodological approach for decision- making support to set up and operate such a system, taking into account the trade-off between monetary cost and service quality. In particular, we compute a lower bound for the infrastructure expenditure required to achieve a certain expected delivery time. The approach includes three time horizons: long-term decisions on the number of depots to deploy in the service area, mid- term decisions on the number of vehicles to use, and short-term decisions on the policy to operate the vehicles

Inspection of Ship Hulls with Multiple UAVs: Exploiting Prior Information for Online Path Planning

International audienceThis paper addresses a path planning problem for a fleet of Unmanned Aerial Vehicles (UAVs) that uses both prior information and online gathered data to efficiently inspect large surfaces such as ship hulls and water tanks. UAVs can detect corrosion patches and other defects on the surface from low-resolution images. If defects are detected, they get closer to the surface for a high-resolution inspection. The prior information provides expected defects locations and is affected by both false positives and false negatives. The mission objective is to prioritize the close-up inspection of defected areas while keeping a reasonable time for the coverage of the entire surface. We propose two solutions to this problem: a coverage algorithm that divides the problem into a set of Traveling Salesman Problems (Part-TSP) and a cooperative frontier approach that introduces frontier utilities to incorporate the prior information (Coop-Frontier). We finally provide extensive simulation results to analyze the performance of these approaches and compare them with alternative solutions. These results suggest that both Part-TSP and Coop-Frontier perform better than the baseline solution. Part-TSP has the best performance in most cases. However, coop-Frontier is preferable in extreme cases because more robust to inhomogeneous corrosion distribution and imperfect information

How the Case/Project based approach works in a Web 2.0 Learning Laboratory

This paper reviews the concepts of Case-based Learning approach in a learning laboratory for developing new managerial competencies. It presents a model of an Applied Learning Laboratory (ALL) characterized by the adoption of Web 2.0 technologies. Within the ALL learners interact with mentors and peers posting comments, evaluating and rating peers' deliverables, providing constructive feedback in order to improve their and other teams' performance. Learners are given the possibility to build their own personal or team blogs, where they can post learning resources, external links, documents, audio and video material. The ALL model is designed according to three main dimensions: the learning approach, the learning content and the learning process. It represents a methodological basis for implementing a platform based on Web 2.0 technologies, which we consider important enablers to develop skills and attitudes such as: virtual communication and collaboration, creative thinking and problem solvin

Inspection of Ship Hulls with Multiple UAVs: Exploiting Prior Information for Online Path Planning

International audienceThis paper addresses a path planning problem for a fleet of Unmanned Aerial Vehicles (UAVs) that uses both prior information and online gathered data to efficiently inspect large surfaces such as ship hulls and water tanks. UAVs can detect corrosion patches and other defects on the surface from low-resolution images. If defects are detected, they get closer to the surface for a high-resolution inspection. The prior information provides expected defects locations and is affected by both false positives and false negatives. The mission objective is to prioritize the close-up inspection of defected areas while keeping a reasonable time for the coverage of the entire surface. We propose two solutions to this problem: a coverage algorithm that divides the problem into a set of Traveling Salesman Problems (Part-TSP) and a cooperative frontier approach that introduces frontier utilities to incorporate the prior information (Coop-Frontier). We finally provide extensive simulation results to analyze the performance of these approaches and compare them with alternative solutions. These results suggest that both Part-TSP and Coop-Frontier perform better than the baseline solution. Part-TSP has the best performance in most cases. However, coop-Frontier is preferable in extreme cases because more robust to inhomogeneous corrosion distribution and imperfect information

Inspection of Ship Hulls with Multiple UAVs: Exploiting Prior Information for Online Path Planning

International audienceThis paper addresses a path planning problem for a fleet of Unmanned Aerial Vehicles (UAVs) that uses both prior information and online gathered data to efficiently inspect large surfaces such as ship hulls and water tanks. UAVs can detect corrosion patches and other defects on the surface from low-resolution images. If defects are detected, they get closer to the surface for a high-resolution inspection. The prior information provides expected defects locations and is affected by both false positives and false negatives. The mission objective is to prioritize the close-up inspection of defected areas while keeping a reasonable time for the coverage of the entire surface. We propose two solutions to this problem: a coverage algorithm that divides the problem into a set of Traveling Salesman Problems (Part-TSP) and a cooperative frontier approach that introduces frontier utilities to incorporate the prior information (Coop-Frontier). We finally provide extensive simulation results to analyze the performance of these approaches and compare them with alternative solutions. These results suggest that both Part-TSP and Coop-Frontier perform better than the baseline solution. Part-TSP has the best performance in most cases. However, coop-Frontier is preferable in extreme cases because more robust to inhomogeneous corrosion distribution and imperfect information