A multi-parent genetic algorithm for solving longitude–latitude-based 4D traveling salesman problems under uncertainty

In this study, we propose a mathematical model of a 4D clustered traveling salesman problem (CTSP) to address the cost-effective security and risk-related difficulties associated with the TSP. We used a multiparent-based memetic genetic algorithm to optimize paths between all clusters and proposed unique heuristic approaches to create clusters and reconnect them. We constructed a 4D CTSP considering multiple routes between two locations and multiple available vehicles on each route. Travel expenses and risks impact every itinerary; however, the behaviors of these costs and risks are always uncertain. We inspected various standard benchmark problems from (TSPLIB) using the proposed calculations. Real-life problems in the tourism industry motivate a longitude–latitude-based CTSP with risk constraints. Thus, we determined the risk of each path based on longitude and latitude. The contributions of this study are twofold: developing a genetic algorithm and heuristics based on mathematical modeling of a real problem.</p

The Dynamic Multi-objective Multi-vehicle Covering Tour Problem

This work introduces a new routing problem called the Dynamic Multi-Objective Multi-vehicle Covering Tour Problem (DMOMCTP). The DMOMCTPs is a combinatorial optimization problem that represents the problem of routing multiple vehicles to survey an area in which unpredictable target nodes may appear during execution. The formulation includes multiple objectives that include minimizing the cost of the combined tour cost, minimizing the longest tour cost, minimizing the distance to nodes to be covered and maximizing the distance to hazardous nodes. This study adapts several existing algorithms to the problem with several operator and solution encoding variations. The efficacy of this set of solvers is measured against six problem instances created from existing Traveling Salesman Problem instances which represent several real countries. The results indicate that repair operators, variable length solution encodings and variable-length operators obtain a better approximation of the true Pareto front

Long-term Informative Path Planning with Autonomous Soaring

The ability of UAVs to cover large areas efficiently is valuable for information gathering missions. For long-term information gathering, a UAV may extend its endurance by accessing energy sources present in the atmosphere. Thermals are a favourable source of wind energy and thermal soaring is adopted in this thesis to enable long-term information gathering. This thesis proposes energy-constrained path planning algorithms for a gliding UAV to maximise information gain given a mission time that greatly exceeds the UAV's endurance. This thesis is motivated by the problem of probabilistic target-search performed by an energy-constrained UAV, which is tasked to simultaneously search for a lost ground target and explore for thermals to regain energy. This problem is termed informative soaring (IFS) and combines informative path planning (IPP) with energy constraints. IFS is shown to be NP-hard by showing that it has a similar problem structure to the weight-constrained shortest path problem with replenishments. While an optimal solution may not exist in polynomial time, this thesis proposes path planning algorithms based on informed tree search to find high quality plans with low computational cost. This thesis addresses complex probabilistic belief maps and three primary contributions are presented: • First, IFS is formulated as a graph search problem by observing that any feasible long-term plan must alternate between 1) information gathering between thermals and 2) replenishing energy within thermals. This is a first step to reducing the large search state space. • The second contribution is observing that a complex belief map can be viewed as a collection of information clusters and using a divide and conquer approach, cluster tree search (CTS), to efficiently find high-quality plans in the large search state space. In CTS, near-greedy tree search is used to find locally optimal plans and two global planning versions are proposed to combine local plans into a full plan. Monte Carlo simulation studies show that CTS produces similar plans to variations of exhaustive search, but runs five to 20 times faster. The more computationally efficient version, CTSDP, uses dynamic programming (DP) to optimally combine local plans. CTSDP is executed in real time on board a UAV to demonstrate computational feasibility. • The third contribution is an extension of CTS to unknown drifting thermals. A thermal exploration map is created to detect new thermals that will eventually intercept clusters, and therefore be valuable to the mission. Time windows are computed for known thermals and an optimal cluster visit schedule is formed. A tree search algorithm called CTSDrift combines CTS and thermal exploration. Using 2400 Monte Carlo simulations, CTSDrift is evaluated against a Full Knowledge method that has full knowledge of the thermal field and a Greedy method. On average, CTSDrift outperforms Greedy in one-third of trials, and achieves similar performance to Full Knowledge when environmental conditions are favourable

Algorithms for multi-robot systems on the cooperative exploration & last-mile delivery problems

La aparición de los vehículos aéreos no tripulados (UAVs) y de los vehículos terrestres no tripulados (UGVs) ha llevado a la comunidad científica a enfrentarse a problemas ideando paradigmas de cooperación con UGVs y UAVs. Sin embargo, no suele ser trivial determinar si la cooperación entre UGVs y UAVs es adecuada para un determinado problema. Por esta razón, en esta tesis, investigamos un paradigma particular de cooperación UGV-UAV en dos problemas de la literatura, y proponemos un controlador autónomo para probarlo en escenarios simulados. Primero, formulamos un problema particular de exploración cooperativa que consiste en alcanzar un conjunto de puntos de destino en un área de exploración a gran escala. Este problema define al UGV como una estación de carga móvil para transportar el UAV a través de diferentes lugares desde donde el UAV puede alcanzar los puntos de destino. Por consiguiente, proponemos el algoritmo TERRA para resolverlo. Este algoritmo se destaca por dividir el problema de exploración en cinco subproblemas, en los que cada subproblema se resuelve en una etapa particular del algoritmo. Debido a la explosión de la entrega de paquetes en las empresas de comercio electrónico, formulamos también una generalización del conocido problema de la entrega en la última milla. En este caso, el UGV actúa como una estación de carga móvil que transporta a los paquetes y a los UAVs, y estos se encargan de entregarlos. De esta manera, seguimos la estrategia de división descrita por TERRA, y proponemos el algoritmo COURIER. Este algoritmo replica las cuatro primeras etapas de TERRA, pero construye una nueva quinta etapa para producir un plan de tareas que resuelva el problema. Para evaluar el paradigma de cooperación UGV-UAV en escenarios simulados, proponemos el controlador autónomo ARIES. Este controlador sigue un enfoque jerárquico descentralizado de líder-seguidor para integrar cualquier paradigma de cooperación de manera distribuida. Ambos algoritmos han sido caracterizados para identificar los aspectos relevantes del paradigma de cooperación en los problemas relacionados. Además, ambos demuestran un gran rendimiento del paradigma de cooperación en tales problemas, y al igual que el controlador autónomo, revelan un gran potencial para futuras aplicaciones reales.The emergence of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) has conducted the research community to face historical complex problems by devising UGV-UAV cooperation paradigms. However, it is usually not a trivial task to determine whether or not a UGV-UAV cooperation is suitable for a particular problem. For this reason, in this thesis, we investigate a particular UGV-UAV cooperation paradigm over two problems in the literature, and we propose an autonomous controller to test it on simulated scenarios. Driven by the planetary exploration, we formulate a particular cooperative exploration problem consisting of reaching a set of target points in a large-scale exploration area. This problem defines the UGV as a moving charging station to carry the UAV through different locations from where the UAV can reach the target points. Consequently, we propose the cooperaTive ExploRation Routing Algorithm (TERRA) to solve it. This algorithm stands out for splitting up the exploration problem into five sub-problems, in which each sub-problem is solved in a particular stage of the algorithm. In the same way, driven by the explosion of parcels delivery in e-commerce companies, we formulate a generalization of the well-known last-mile delivery problem. This generalization defines the same UGV’s and UAV’s rol as the exploration problem. That is, the UGV acts as a moving charging station which carries the parcels along several UAVs to deliver them. In this way, we follow the split strategy depicted by TERRA to propose the COoperative Unmanned deliveRIEs planning algoRithm (COURIER). This algorithm replicates the first four TERRA’s stages, but it builds a new fifth stage to produce a task plan solving the problem. In order to evaluate the UGV-UAV cooperation paradigm on simulated scenarios, we propose the Autonomous coopeRatIve Execution System (ARIES). This controller follows a hierarchical decentralized leader-follower approach to integrate any cooperation paradigm in a distributed manner. Both algorithms have been characterized to identify the relevant aspects of the cooperation paradigm in the related problems. Also, both of them demonstrate a great performance of the cooperation paradigm in such problems, and as well as the autonomous controller, reveal a great potential for future real applications

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Coverage & cooperation: Completing complex tasks as quickly as possible using teams of robots

As the robotics industry grows and robots enter our homes and public spaces, they are increasingly expected to work in cooperation with each other. My thesis focuses on multirobot planning, specifically in the context of coverage robots, such as robotic lawnmowers and vacuum cleaners. Two problems unique to multirobot teams are task allocation and search. I present a task allocation algorithm which balances the workload amongst all robots in the team with the objective of minimizing the overall mission time. I also present a search algorithm which robots can use to find lost teammates. It uses a probabilistic belief of a target robot’s position to create a planning tree and then searches by following the best path in the tree. For robust multirobot coverage, I use both the task allocation and search algorithms. First the coverage region is divided into a set of small coverage tasks which minimize the number of turns the robots will need to take. These tasks are then allocated to individual robots. During the mission, robots replan with nearby robots to rebalance the workload and, once a robot has finished its tasks, it searches for teammates to help them finish their tasks faster

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

A Complete Coverage Algorithm for 3D Structural Inspection using an Autonomous Unmanned Aerial Vehicle

This thesis presents a novel algorithm for complete coverage of three-dimensional structures to address the problem of autonomous structural inspection using an Unmanned Aerial Vehicle (UAV). The proposed approach uses a technique of cellular decomposition based on Morse decomposition to decompose the 3D target structure into 2D coverable faces that are subsequently connected using a graph-based representation. We then use graph traversal techniques such as the Traveling Salesman Problem (TSP) to generate a flight coverage path through the decomposed faces for a UAV to completely cover the target structure, while reducing the coverage time and distance. To test the validity of our proposed approach, we have performed a series of experiments using a simulated AscTec Firefly UAV in different environments with 3D structures of different sizes and geometries, within the Robot Operating System (ROS) Gazebo simulator. Our results show that our approach guarantees complete coverage of the target structure. Comparison of our coverage strategy with other strategies shows that our proposed TSP-based coverage strategy performs up to 50% better in reducing the flight path with an average of 30% fewer turns and 12% less coverage duration than a largest-area-first approach