Downwash-Aware Trajectory Planning for Large Quadrotor Teams

We describe a method for formation-change trajectory planning for large quadrotor teams in obstacle-rich environments. Our method decomposes the planning problem into two stages: a discrete planner operating on a graph representation of the workspace, and a continuous refinement that converts the non-smooth graph plan into a set of C^k-continuous trajectories, locally optimizing an integral-squared-derivative cost. We account for the downwash effect, allowing safe flight in dense formations. We demonstrate the computational efficiency in simulation with up to 200 robots and the physical plausibility with an experiment with 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes.Comment: 8 page

Production Scheduling

Generally speaking, scheduling is the procedure of mapping a set of tasks or jobs (studied objects) to a set of target resources efficiently. More specifically, as a part of a larger planning and scheduling process, production scheduling is essential for the proper functioning of a manufacturing enterprise. This book presents ten chapters divided into five sections. Section 1 discusses rescheduling strategies, policies, and methods for production scheduling. Section 2 presents two chapters about flow shop scheduling. Section 3 describes heuristic and metaheuristic methods for treating the scheduling problem in an efficient manner. In addition, two test cases are presented in Section 4. The first uses simulation, while the second shows a real implementation of a production scheduling system. Finally, Section 5 presents some modeling strategies for building production scheduling systems. This book will be of interest to those working in the decision-making branches of production, in various operational research areas, as well as computational methods design. People from a diverse background ranging from academia and research to those working in industry, can take advantage of this volume

Mathematical Modelling and Methods for Load Balancing and Coordination of Multi-Robot Stations

The automotive industry is moving from mass production towards an individualized production, individualizing parts aims to improve product quality and to reduce costs and material waste. This thesis concerns aspects of load balancing and coordination of multi-robot stations in the automotive manufacturing industry, considering efficient algorithms required by an individualized production. The goal of the load balancing problem is to improve the equipment utilization. Several approaches for solving the load balancing problem are suggested along with details on mathematical tools and subroutines employed.Our contributions to the solution of the load balancing problem are fourfold. First, to circumvent robot coordination we construct disjoint robot programs, which require no coordination schemes, are flexible, admit competitive cycle times for several industrial instances, and may be preferred in an individualized production. Second, since solving the task assignment problem for generating the disjoint robot programs was found to be unreasonably time-consuming, we model it as a generalized unrelated parallel machine problem with set packing constraints and suggest a tailored Lagrangian-based branch-and-bound algorithm. Third, a continuous collision detection method needs to determine whether the sweeps of multiple moving robots are disjoint. We suggest using the maximum velocity of each robot along with distance computations at certain robot configurations to derive a function that provides lower bounds on the minimum distance between the sweeps. The lower bounding function is iteratively minimized and updated with new distance information; our method is substantially faster than previously developed methods. Fourth, to allow for load balancing of complex multi-robot stations we generalize the disjoint robot programs into sequences of such; for some instances this procedure provides a significant equipment utilization improvement in comparison with previous automated methods

Mathematical Modelling for Load Balancing and Minimization of Coordination Losses in Multirobot Stations

The automotive industry is moving from mass production towards an individualized production, in order to improve product quality and reduce costs and material waste. This thesis concerns aspects of load balancing of industrial robots in the automotive manufacturing industry, considering efficient algorithms required by an individualized production. The goal of the load balancing problem is to improve the equipment utilization. Several approaches for solving the load balancing problem are presented along with details on mathematical tools and subroutines employed.Our contributions to the solution of the load balancing problem are manifold. First, to circumvent robot coordination we have constructed disjoint robot programs, which require no coordination schemes, are more flexible, admit competitive cycle times for some industrial instances, and may be preferred in an individualized production. Second, since solving the task assignment problem for generating the disjoint robot programs was found to be unreasonably time-consuming, we modelled it as a generalized unrelated parallel machine problem with set packing constraints and suggested a tighter model formulation, which was proven to be much more tractable for a branch--and--cut solver. Third, within continuous collision detection it needs to be determined whether the sweeps of multiple moving robots are disjoint. Our solution uses the maximum velocity of each robot along with distance computations at certain robot configurations to derive a function that provides lower bounds on the minimum distance between the sweeps. The lower bounding function is iteratively minimized and updated with new distance information; our method is substantially faster than previously developed methods

Multi-agent Coordination Under Temporal Logic Tasks and Team-Wise Intermittent Communication

Multi-agent systems outperform single agent in complex collaborative tasks. However, in large-scale scenarios, ensuring timely information exchange during decentralized task execution remains a challenge. This work presents an online decentralized coordination scheme for multi-agent systems under complex local tasks and intermittent communication constraints. Unlike existing strategies that enforce all-time or intermittent connectivity, our approach allows agents to join or leave communication networks at aperiodic intervals, as deemed optimal by their online task execution. This scheme concurrently determines local plans and refines the communication strategy, i.e., where and when to communicate as a team. A decentralized potential game is modeled among agents, for which a Nash equilibrium is generated iteratively through online local search. It guarantees local task completion and intermittent communication constraints. Extensive numerical simulations are conducted against several strong baselines.Comment: 6 pages, 2 figure

Multi-agent path planning for mobile robots with discrete-step locomotion

The \u201cswing-and-dock\u201d (SaD) model for realizing displacements has been invented for and is used by the mobile robotic fixtures developed in the SwarmItFix European project. This form of locomotion can be a valuable capability for material handling agents, and fixturing agents enabling simultaneous handling in a non-linear fashion and increasing manufacturing flexibility. The thesis focuses on the design of SaD path planning algorithms for the motion of the agent

Resource Allocation in SDN/NFV-Enabled Core Networks

For next generation core networks, it is anticipated to integrate communication, storage and computing resources into one unified, programmable and flexible infrastructure. Software-defined networking (SDN) and network function virtualization (NFV) become two enablers. SDN decouples the network control and forwarding functions, which facilitates network management and enables network programmability. NFV allows the network functions to be virtualized and placed on high capacity servers located anywhere in the network, not only on dedicated devices in current networks. Driven by SDN and NFV platforms, the future network architecture is expected to feature centralized network management, virtualized function chaining, reduced capital and operational costs, and enhanced service quality. The combination of SDN and NFV provides a potential technical route to promote the future communication networks. It is imperative to efficiently manage, allocate and optimize the heterogeneous resources, including computing, storage, and communication resources, to the customized services to achieve better quality-of-service (QoS) provisioning. This thesis makes some in-depth researches on efficient resource allocation for SDN/NFV-enabled core networks in multiple aspects and dimensionality. Typically, the resource allocation task is implemented in three aspects. Given the traffic metrics, QoS requirements, and resource constraints of the substrate network, we first need to compose a virtual network function (VNF) chain to form a virtual network (VN) topology. Then, virtual resources allocated to each VNF or virtual link need to be optimized in order to minimize the provisioning cost while satisfying the QoS requirements. Next, we need to embed the virtual network (i.e., VNF chain) onto the substrate network, in which we need to assign the physical resources in an economical way to meet the resource demands of VNFs and links. This involves determining the locations of NFV nodes to host the VNFs and the routing from source to destination. Finally, we need to schedule the VNFs for multiple services to minimize the service completion time and maximize the network performance. In this thesis, we study resource allocation in SDN/NFV-enabled core networks from the aforementioned three aspects. First, we jointly study how to design the topology of a VN and embed the resultant VN onto a substrate network with the objective of minimizing the embedding cost while satisfying the QoS requirements. In VN topology design, optimizing the resource requirement for each virtual node and link is necessary. Without topology optimization, the resources assigned to the virtual network may be insufficient or redundant, leading to degraded service quality or increased embedding cost. The joint problem is formulated as a Mixed Integer Nonlinear Programming (MINLP), where queueing theory is utilized as the methodology to analyze the network delay and help to define the optimal set of physical resource requirements at network elements. Two algorithms are proposed to obtain the optimal/near-optimal solutions of the MINLP model. Second, we address the multi-SFC embedding problem by a game theoretical approach, considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. In the proposed resource constrained multi-SFC embedding game (RC-MSEG), each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all its NFV nodes. Due to processing-resource sharing, additional delay is incurred and integrated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We first prove that the proposed game RC-MSEG is an exact potential game admitting at least one pure Nash Equilibrium (NE) and has the finite improvement property (FIP). Then, we design two iterative algorithms, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE of the proposed game. Third, the VNF scheduling problem is investigated to minimize the makespan (i.e., overall completion time) of all services, while satisfying their different end-to-end (E2E) delay requirements. The problem is formulated as a mixed integer linear program (MILP) which is NP-hard with exponentially increasing computational complexity as the network size expands. To solve the MILP with high efficiency and accuracy, the original problem is reformulated as a Markov decision process (MDP) problem with variable action set. Then, a reinforcement learning (RL) algorithm is developed to learn the best scheduling policy by continuously interacting with the network environment. The proposed learning algorithm determines the variable action set at each decision-making state and accommodates different execution time of the actions. The reward function in the proposed algorithm is carefully designed to realize delay-aware VNF scheduling. To sum up, it is of great importance to integrate SDN and NFV in the same network to accelerate the evolution toward software-enabled network services. We have studied VN topology design, multi-VNF chain embedding, and delay-aware VNF scheduling to achieve efficient resource allocation in different dimensions. The proposed approaches pave the way for exploiting network slicing to improve resource utilization and facilitate QoS-guaranteed service provisioning in SDN/NFV-enabled networks