Motion Planning of UAV Swarm: Recent Challenges and Approaches

The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

Multiple UAV systems: a survey

Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of tasks. There are few research works that cover the applications of these systems and they are all highly specialized. The goal of this survey is to fill this gap by providing a generic review on different applications of multiple UAV systems that have been developed in recent years. We also present a nomenclature and architecture taxonomy for these systems. In the end, a discussion on current trends and challenges is provided.This work was funded by the Ministry of Economy, Industryand Competitiveness of Spain under Grant Nos. TRA2016-77012-R and BES-2017-079798Peer ReviewedPostprint (published version

Computational Frameworks for Multi-Robot Cooperative 3D Printing and Planning

This dissertation proposes a novel cooperative 3D printing (C3DP) approach for multi-robot additive manufacturing (AM) and presents scheduling and planning strategies that enable multi-robot cooperation in the manufacturing environment. C3DP is the first step towards achieving the overarching goal of swarm manufacturing (SM). SM is a paradigm for distributed manufacturing that envisions networks of micro-factories, each of which employs thousands of mobile robots that can manufacture different products on demand. SM breaks down the complicated supply chain used to deliver a product from a large production facility from one part of the world to another. Instead, it establishes a network of geographically distributed micro-factories that can manufacture the product at a smaller scale without increasing the cost. In C3DP, many printhead-carrying mobile robots work together to print a single part cooperatively. While it holds the promise to mitigate issues associated with gantry-based 3D printers, such as lack of scalability in print size and print speed, its realization is challenging because existing studies in the relevant literature do not address the fundamental issues in C3DP that stem from the amalgamation of the mobile nature of the robots, and continuous nature of the manufacturing tasks. To address this challenge, this dissertation asks two fundamental research questions: RQ1) How can the traditional 3D printing process be transformed to enable multi-robot cooperative AM? RQ2) How can cooperative manufacturing planning be realized in the presence of inherent uncertainties in AM and constraints that are dynamic in both space and time? To answer RQ1, we discretize the process of 3D printing into multiple stages. These stages include chunking (dividing a part into smaller chunks), scheduling (assigning chunks to robots and generating print sequences), and path and motion planning. To test the viability of the approach, we conducted a study on the tensile strength of chunk-based parts to examine their mechanical integrity. The study demonstrates that the chunk-based part can be as strong as the conventionally 3D-printed part. Next, we present different computational frameworks to address scheduling issues in C3DP. These include the development of 1) the world-first working strategy for C3DP, 2) a framework for automatic print schedule generation, evaluation, and validation, and 3) a resource-constrained scheduling approach for C3DP that uses a meta-heuristic approach such as a modified Genetic Algorithm (MGA) and a new algorithm that uses a constraint-satisficing approach to obtain collision-free print schedules for C3DP. To answer RQ2, a multi-robot decentralized approach based on a simple set of rules is used to plan for C3DP. The approach is resilient to uncertainties such as variation in printing times and can even outperform the centralized approach that uses MGA with a conflict-based search for large-scale problems. By answering these two fundamental questions, the central objective of the research project to establish computational frameworks to enable multi-robot cooperative manufacturing was achieved. The search for answers to the RQs led to the development of novel concepts that can be used not only in C3DP, but many other manufacturing tasks, in general, requiring cooperation among multiple robots

Planning and reconfigurable control of a fleet of unmanned vehicles for taxi operations in airport environment

The optimization of airport operations has gained increasing interest by the aeronautical community, due to the substantial growth in the number of airport movements (landings and take-offs) experienced in the past decades all over the world. Forecasts have confirmed this trend also for the next decades. The result of the expansion of air traffic is an increasing congestion of airports, especially in taxiways and runways, leading to additional amount of fuel burnt by airplanes during taxi operations, causing additional pollution and costs for airlines. In order to reduce the impact of taxi operations, different solutions have been proposed in literature; the solution which this dissertation refers to uses autonomous electric vehicles to tow airplanes between parking lots and runways. Although several analyses have been proposed in literature, showing the feasibility and the effectiveness of this approach in reducing the environmental impact, at the beginning of the doctoral activity no solutions were proposed, on how to manage the fleet of unmanned vehicles inside the airport environment. Therefore, the research activity has focused on the development of algorithms able to provide pushback tractor (also referred as tugs) autopilots with conflict-free schedules. The main objective of the optimization algorithms is to minimize the tug energy consumption, while performing just-in-time runway operations: departing airplanes are delivered only when they can take-off and the taxi-in phase starts as soon as the aircraft clears the runway and connects to the tractor. Two models, one based on continuous time and one on discrete time evolution, were developed to simulate the taxi phases within the optimization scheme. A piecewise-linear model has also been proposed to evaluate the energy consumed by the tugs during the assigned missions. Furthermore, three optimization algorithms were developed: two hybrid versions of the particle swarm optimization and a tree search heuristic. The following functional requirements for the management algorithm were defined: the optimization model must be easily adapted to different airports with different layout (reconfigurability); the generated schedule must always be conflict-free; and the computational time required to process a time horizon of 1h must be less than 15min. In order to improve its performance, the particle swarm optimization was hybridized with a hill-climb meta-heuristic; a second hybridization was performed by means of the random variable search, an algorithm of the family of the variable neighborhood search. The neighborhood size for the random variable search was considered varying with inverse proportionality to the distance between the actual considered solution and the optimal one found so far. Finally, a tree search heuristic was developed to find the runway sequence, among all the possible sequences of take-offs and landings for a given flight schedule, which can be realized with a series of taxi trajectories that require minimum energy consumption. Given the taxi schedule generated by the aforementioned optimization algorithms a tug dispatch algorithm, assigns a vehicle to each mission. The three optimization schemes and the two mathematical models were tested on several test cases among three airports: the Turin-Caselle airport, the Milan-Malpensa airport, and the Amsterdam airport Schiphol. The cost required to perform the generated schedules using the autonomous tugs was compared to the cost required to perform the taxi using the aircraft engines. The proposed approach resulted always more convenient than the classical one

Ant-inspired Interaction Networks For Decentralized Vehicular Traffic Congestion Control

Mimicking the autonomous behaviors of animals and their adaptability to changing or foreign environments lead to the development of swarm intelligence techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) now widely used to tackle a variety of optimization problems. The aim of this dissertation is to develop an alternative swarm intelligence model geared toward decentralized congestion avoidance and to determine qualities of the model suitable for use in a transportation network. A microscopic multi-agent interaction network inspired by insect foraging behaviors, especially ants, was developed and consequently adapted to prioritize the avoidance of congestion, evaluated as perceived density of other agents in the immediate environment extrapolated from the occurrence of direct interactions between agents, while foraging for food outside the base/nest. The agents eschew pheromone trails or other forms of stigmergic communication in favor of these direct interactions whose rate is the primary motivator for the agents\u27 decision making process. The decision making process at the core of the multi-agent interaction network is consequently transferred to transportation networks utilizing vehicular ad-hoc networks (VANETs) for communication between vehicles. Direct interactions are replaced by dedicated short range communications for wireless access in vehicular environments (DSRC/WAVE) messages used for a variety of applications like left turn assist, intersection collision avoidance, or cooperative adaptive cruise control. Each vehicle correlates the traffic on the wireless network with congestion in the transportation network and consequently decides whether to reroute and, if so, what alternate route to take in a decentralized, non-deterministic manner. The algorithm has been shown to increase throughput and decrease mean travel times significantly while not requiring access to centralized infrastructure or up-to-date traffic information

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

A hybrid multi-objective evolutionary algorithm-based semantic foundation for sustainable distributed manufacturing systems

Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.The project is funded by Department of Science and Technology, Science and Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT–Portuguese Foundation for Science and Technology within the R&D Units Projects Scopes: UIDB/00319/2020, UIDP/04077/2020, and UIDB/04077/2020