Optimizing Fuel-Constrained UAV-UGV Routes for Large Scale Coverage: Bilevel Planning in Heterogeneous Multi-Agent Systems

Fast moving unmanned aerial vehicles (UAVs) are well suited for aerial surveillance, but are limited by their battery capacity. To increase their endurance UAVs can be refueled on slow moving unmanned ground vehicles (UGVs). The cooperative routing of UAV-UGV multi-agent system to survey vast regions within their speed and fuel constraints is a computationally challenging problem, but can be simplified with heuristics. Here we present multiple heuristics to enable feasible and sufficiently optimal solutions to the problem. Using the UAV fuel limits and the minimum set cover algorithm, the UGV refueling stops are determined. These refueling stops enable the allocation of mission points to the UAV and UGV. A standard traveling salesman formulation and a vehicle routing formulation with time windows, dropped visits, and capacity constraints is used to solve for the UGV and UAV route, respectively. Experimental validation on a small-scale testbed (http://tiny.cc/8or8vz) underscores the effectiveness of our multi-agent approach.Comment: The paper is submitted to MRS 202

Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand

Humanitarian logistics service providers have two major responsibilities immediately after a disaster: locating trapped people and routing aid to them. These difficult operations are further hindered by failures in the transportation and telecommunications networks, which are often rendered unusable by the disaster at hand. In this work, we propose two-echelon vehicle routing frameworks for performing these operations using aerial uncrewed autonomous vehicles (UAVs or drones) to address the issues associated with these failures. In our proposed frameworks, we assume that ground vehicles cannot reach the trapped population directly, but they can only transport drones from a depot to some intermediate locations. The drones launched from these locations serve to both identify demands for medical and other aids (e.g., epi-pens, medical supplies, dry food, water) and make deliveries to satisfy them. Specifically, we present two decision frameworks, in which the resulting optimization problem is formulated as a two-echelon vehicle routing problem. The first framework addresses the problem in two stages: providing telecommunications capabilities in the first stage and satisfying the resulting demands in the second. To that end, two types of drones are considered. Hotspot drones have the capability of providing cell phone and internet reception, and hence are used to capture demands. Delivery drones are subsequently employed to satisfy the observed demand. The second framework, on the other hand, addresses the problem as a stochastic emergency aid delivery problem, which uses a two-stage robust optimization model to handle demand uncertainty. To solve the resulting models, we propose efficient and novel solution approaches

The Application of Drones in City Logistics Concepts

With the rise of city logistics (CL) problems in the last three decades, various methods, approaches, solutions, and initiatives were analyzed and proposed for making logistics in urban areas more sustainable. The most analyzed and promising solutions are those that take into account cooperation among logistics providers and consolidation of the flow of goods. Furthermore, technological innovations enable the implementation of modern vehicles/equipment in order to make CL solutions sustainable. For several years, drone-based delivery has attracted lots of attention in scientific research, but there is a serious gap in the literature regarding the application of drones in CL concepts. The goal of this paper is to analyze four CL concepts that differ in consolidation type, transformation degree of flow of goods (direct and indirect, multi-echelon flows), and the role of drones. Two of the analyzed concepts are novel, which is the main contribution of the paper. The performances of the analyzed concepts are compared to the performances of the traditional delivery model – using only trucks without prior flow consolidation. The results indicate that CL concepts which combine different consolidation models and drones in the last phase of the delivery could stand out as a sustainable CL solution

A Tandem Drone-ground Vehicle for Accessing Isolated Locations for First Aid Emergency Response in Case of Disaster

The collapse of infrastructures is very often a complicating factor for the early emergency actuations after a disaster. A proper plan to better cover the needs of the affected people within the disaster area while maintaining life-saving relief operations is mandatory hence. In this paper, we use a drone for flying over a set of difficult-to-access locations for imaging issues to get information to build a risk assessment as the earliest stage of the emergency operations. While the drone provides the flexibility required to visit subsequently a sort of isolated locations, it needs a commando vehicle in ground for (i) monitoring the deployment of operations and (ii) being a recharging station where the drone gets fresh batteries. This work proposes a decision-making process to plan the mission, which is composed by the ground vehicle stopping points and the sequence of locations visited for each drone route. We propose a Genetic Algorithm (GA) which has proven to be helpful in finding good solutions in short computing times. We provide experimental analysis on the factors effecting the performance of the output solutions, around an illustrative test instance. Results show the applicability of these techniques for providing proper solutions to the studied problem

Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots

In addition to its crucial impact on customer satisfaction, last-mile delivery (LMD) is notorious for being the most time-consuming and costly stage of the shipping process. Pressing environmental concerns combined with the recent surge of e-commerce sales have sparked renewed interest in automation and electrification of last-mile logistics. To address the hurdles faced by existing robotic couriers, this paper introduces a customer-centric and safety-conscious LMD system for small urban communities based on AI-assisted autonomous delivery robots. The presented framework enables end-to-end automation and optimization of the logistic process while catering for real-world imposed operational uncertainties, clients' preferred time schedules, and safety of pedestrians. To this end, the integrated optimization component is modeled as a robust variant of the Cumulative Capacitated Vehicle Routing Problem with Time Windows, where routes are constructed under uncertain travel times with an objective to minimize the total latency of deliveries (i.e., the overall waiting time of customers, which can negatively affect their satisfaction). We demonstrate the proposed LMD system's utility through real-world trials in a university campus with a single robotic courier. Implementation aspects as well as the findings and practical insights gained from the deployment are discussed in detail. Lastly, we round up the contributions with numerical simulations to investigate the scalability of the developed mathematical formulation with respect to the number of robotic vehicles and customers

트럭을 이동형 드론 기지로 사용하는 한정용량 트럭-드론 경로 배정 문제

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022.2. 김동규.Drones initially received attention for military purposes as a collective term for unmanned aerial vehicles (UAVs), but recently, efforts to use them in logistics have been actively underway. If drones are put into places where low-weight and high-value items are currently difficult to deliver by existing delivery means, it will have the effect of greatly reducing costs. However, the disadvantages of drones in delivery are also clear. In order to improve the delivery capacity of drones, the size of drones must increase when drones are equipped with large-capacity batteries. This thesis introduced two methods and presented algorithms for each method among VRP-D. First of all, CVP-D is a method in which carriers such as trucks and ships with large capacity and slow speed carry robots and drones with small capacity. Next, in the CVRP-D, the vehicle and the drone move different paths simultaneously, and the drone can visit multiple nodes during one sortie. The two problems are problems in which restrictions are added to the vehicle route problem (VRP), known as the NP-hard problem. The algorithm presented in this study derived drone-truck routes for two problems within a reasonable time. In addition, sensitivity analysis was conducted to observe changes in the appropriate network structure for the introduction of drone delivery and the main parameters of the drone. In addition, the validity of the proposed algorithm was verified through comparison with the data used as a benchmark in previous studies. These research results will contribute to the creation of delivery routes quickly, considering the specification of a drone.드론은 무인항공기(UAV)의 통칭으로 초기에는 군사적 목적으로 주목을 받았으나 최근 물류에서 사용하려는 노력이 적극적으로 진행되고 있다. 드론이 저중량-고가치 물품을 배송에서 현재 기존 배송수단에 의해 배송이 어려운 곳에 투입이 된다면 큰 비용절감의 효과가 있을 것이다. 하지만 배송에 있어서 드론의 단점도 명확하다. 드론의 배송능력을 향상시키기 위해서는 드론이 대용량 배터리를 탑재하면 드론 크기가 증가하여야 한다. 이러한 단점을 극복하기 위해서 드론과 트럭을 결합하여 운영하는 방식이 연구되어왔다. 이러한 방식 중 본 연구에서는 두 가지 방식을 소개하고, 각각의 방식에 대한 알고리즘을 제시하였다. 먼저, CVP-D는 용량이 크고 속도가 느린 트럭이나 배 등의 캐리어가 용량이 작은 로봇, 드론 등을 싣고 다니면서 배송을 하는 방식이다. 다음으로, CVRP-D는 차량과 드론이 동시에 각기 다른 경로를 이동하며, 드론은 1회 비행(sortie)시 다수의 노드를 방문하는 것이 가능하다. 두 문제는 차량경로문제(VRP)에 제약이 더해진 문제이다. VRP는 대표적인 NP-hard 문제로 해를 구하기 위해서 휴리스틱 알고리즘이 요구된다. 본 연구에서 제시하는 알고리즘은 합리적인 시간 내 두문제의 드론-트럭 경로를 도출하였다. 또한 민감도 분석을 실시하여 드론 배송 도입을 위한 적절한 네트워크 구조 및 드론의 주요 파라미터에 변화에 따른 변화를 관찰하였다. 이는 차후 드론의 성능에 관한 의사결정 시 고려해야 할 요소들에 대한 기준이 될 수 있을 것으로 기대된다. 또한 선행연구에서 벤치마크로 사용되는 데이터와의 비교를 통해 제안하는 알고리즘의 타당성을 검증하였다. 본 연구는 드론 도입이 배송시간을 감소시키며, 운영방법에 따라서 배송시간의 차이가 발생함을 보였다. 이러한 연구 성과는 드론 배송 시 환경과 기계적 성능을 고려한 배송 경로를 단시간내 생성하여 상업적으로 이용가능 할 것이다.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Purpose 3 1.3 Contribution of Research 4 Chapter 2. Literature review 5 2.1 Vehicle Routing Problems with Drone 5 2.2 Carrier Vehicle Problem with Drone(CVP-D) 10 2.3 Capacitated VRP with Drone(CVRP-D) 12 Chapter 3. Mathematical Formulation 14 3.1 Terminology 14 3.2 CVP-D Formulation 15 3.3 CVRP-D Formulation 19 Chapter 4. Proposed Algorithms 23 4.1 Heuristic Algorithm 23 4.1.1 Knapsack Problem 23 4.1.2 Parallel Machine Scheduling (PMS) 25 4.1.3 Set Covering Location Problem (SCLP) 27 4.1.4 Guided Local Search (GLS) Algorithm 28 4.1.5 Genetic Algorithm (GA) 29 4.2 Proposed Heuristic Algorithm : GA-CVPD 30 4.3 Proposed Heuristic Algorithm : GA-CVRPD 33 Chapter 5. Numerical Analysis 36 5.1 Data Description 36 5.2 Numerical experiment 37 5.3 Sensitivity analysis 39 5.3.1 Analysis on GA-CVPD 39 5.3.2 Analysis on GA-CVRPD 42 5.3.3 Result on different Instances 45 Chapter 6. Conclusion 48 Bibliography 50 Abstract in Korean 53 4.1.5 Genetic Algorithm (GA) 29 4.2 Proposed Heuristic Algorithm : GA-CVPD 30 4.3 Proposed Heuristic Algorithm : GA-CVRPD 33 Chapter 5. Numerical Analysis 36 5.1 Data Description 36 5.2 Numerical experiment 37 5.3 Sensitivity analysis 42 5.3.1 Analysis on GA-CVPD 39 5.3.2 Analysis on GA-CVRPD 42 5.3.3 Result on different Instances 45 Chapter 6. Conclusion 48 Bibliography 50 Abstract in Korean 53석

Congestion based Truck Drone intermodal delivery optimization

Commerce companies have experienced a rise in the number of parcels that need to be delivered each day. The goal of this study is to provide a decision-making procedure to assist carriers in taking a more significant role in selecting cost and risk-efficient truck-drone intermodal delivery routing plan. The congestion-based model is developed to select the method of parcel delivery utilizing a truck and a drone for optimizing cost and time. A study also has been conducted to compare drone-only and truck-only delivery routing plan. The proposed A* Heuristic algorithm and the OSRM application generate the travel path for drone and a truck along with the time of travel. Case studies have been conducted by varying the weight provided to cost and risk variable, studies indicate that there is a significant change in drone delivery travel time and cost with increase of cost weightage