Autonomous Rescue Robots Research Articles

Design of Sub-Systems for GPS-Guided Autonomous Delivery Robot System

The use of autonomous robots for delivery services is a new potential goldmine. Furthermore, since the e-commerce and delivery industry are growing at a rapid rate, it is recommended that a system that could handle the high-volume traffic as well serve as a new customer attraction, be implemented. Therefore, this work aims to develop the Autonomous Delivery Robot System (ADRS) that could be utilized for delivery services. The ADRS uses the Arduino microcontroller to run a program. The developed system consists of three main components (sub-systems), namely, mobile robot, cloud server and mobile application. The mobile robot is equipped with a tracking and an obstacle detection system to maneuver it autonomously. The ultrasonic sensors are used for obstacle detection, coupled with a Global Positioning System (GPS) to enable it deliver packages to customers in a typical outdoor environment. The ADRS ensures a human-contactless and secure delivery by using a password protected container to carry the delivery packages. Only the customer can unlock the container using the provided password via mobile application. The mobile application with customized map based on Google Maps is created to provide the user with an interactive interface. A camera is also attached on the robot for real-time monitoring purpose. The performance of the ADRS has been evaluated to determine the functionality of the developed system.

Analysis of Modern vs. Conventional Development Technologies in Transportation-The Case Study of a Last-Mile Delivery Process.

Transportation plays a significant role in the global economy and society and takes part in a lot of different processes such as mass transportation and the supply chain. Therefore, it is crucial to introduce modern technologies in this area of the economy in the context of Industry 4.0. The main scope of this study is to develop a model that supports analyzing last-mile logistics modern solutions using the latest technologies such as road autonomous delivery robots (RADRs), civil drones, or smart bikes, and compare them to conventional solutions (delivery vehicles). Multi-criteria decision analysis (MCDA) was applied to build a formal comparison model that scores the solutions and weights different criteria according to decision-makers and placeholders, to rank the solutions from the most crucial option to the weakest in a predetermined scenario with set parameters and conditions (three varied scenarios were included in the present investigation). The results of the model were in favor of using civil drones or smart bicycles to perform light deliveries in small urban areas (these key findings support the assumptions that are often manifested in speech in the context of the use of new technologies). The modern solutions scored almost 40-80% higher in total in the conglomeration of assessment criteria (such as safety, economy, laws and regulations, operation time for the delivery, environment, and payload) than the conventional solution, which indicates the importance of studying the implementation of such technologies. An interesting result of the study is the operational cost reduction by ca. 60-74% in favor of autonomous delivery robots, 89-93% in favor of civil delivery drones, and 87-90% in favor of smart bikes vs. conventional delivery trucks/vans. Yet, it should be underlined that the results may vary with different assumptions within the MCDA method.

Autonomous delivery robots and their contribution during the pandemic

Autonomous delivery robots and their contribution during the pandemic

Physical Internet-enabled E-grocery delivery Network:A load-dependent two-echelon vehicle routing problem with mixed vehicles

Autonomous delivery robots (ADRs) as green last-mile delivery alternatives to traditional vans have received much attention due to the rapid development of Physical Internet (PI). However, their low efficiency limits the application in the last-mile delivery because few orders per trip are delivered. Also, the cost and emission impact of new motilities on the PI-enabled last-mile delivery network is still not clear. To address these issues, this study developed an innovative two-echelon delivery system that combines traditional vans and ADRs to take their advantages for the last-mile delivery. The objective of this study was to minimize transportation costs and emissions through solving an extension of a two-echelon vehicle routing problem considering load-dependent unit transport costs and unit emissions with mixed vehicles and multiple depots. The problem was formulated as a mixed-integer programming model and then efficiently solved by a cluster-based artificial immune algorithm, in which an improved clustering method was employed to assign the customers. The experimental results showed that the proposed solution approach efficiently solved the problem with better solutions than existing approaches in the relevant literature. Several managerial implications on configuring the two-echelon delivery system were also provided for potential applications.

Consumer adoption of autonomous delivery robots in cities: Implications on urban planning and design policies

Five hundred survey responses on consumer acceptance of autonomous delivery robots (ADRs) were collected because the pandemic has increased the emphasis on contactless deliveries, spurring some interest in ADRs to perform last-mile deliveries in urban cities. To examine consumers' intention to adopt ADRs, a comprehensive theoretical model grounded on the Health Belief Model and Task-Technology Fit Model was presented and structural equation modeling was applied to examine the survey data. The analysis revealed that the constructs from both theories have significant effects on outcome expectations and task-technology fit. Additionally, outcome expectations and task-technology fit are strong predictors of consumers' intention, as indicated by their direct and indirect effects. Thus, this study enriches existing research by interpreting consumers' intention to adopt ADRs through health and technology perspectives. It also provides practical implications and policy recommendations for urban planning and design.

Ready for robots? Assessment of autonomous delivery robot operative accessibility in German cities

Unlike autonomous car applications, the operational area of urban service autonomous robots like autonomous delivery robots (ADRs) is not clearly defined at the moment. Due to large variations in the different robot designs, specific local infrastructure and regulation, assessing the feasibility of different operational scenarios is difficult. This paper presents a prototype evaluation methodology based on Open Street Map data for the assessment of ADR deployments considering one-to-many delivery schemes. Four different robot configurations and potential operational specifications are modeled and evaluated in a sample of German cities. The bandwidth of considered robot types ranges from large ADRs operating on roadways down to small size systems operating on sidewalks. The performance of the first category is limited by the reduced accessibility in areas with higher traffic. On the contrary, small ADRs present a higher detour time but increased accessibility. The evaluated operational scenarios show very diverse performance depending on the considered metrics and cities. For all the metrics considered in the paper, sidewalk ADRs show poor performance when compared to other potential ADR deployments.

Acceptance of autonomous delivery robots in urban cities

Autonomous Delivery Robots (ADR), an innovative last-mile delivery method, can be seen as a sustainable solution for the distribution of goods in urban cities. This study combines the modified-TAM (encompassing the Technology Acceptance Model (TAM) and threat elements of the health belief model) and the Theory of Planned Behaviour (TPB) into the stimulus-organism-response framework to explain consumer intention to use ADRs. The responses of 500 respondents living in Singapore were collected via an online questionnaire, and the collected results were obtained using structural equation modelling. Using the modified-TAM and TPB constructs as the main conceptual framework for analysis, the results show significant results for consumers' ADR usage. Based on total effects analysis, attitude shows the largest effect on consumers' intention to use ADRs, followed by perceived usefulness, perceived susceptibility, perceived severity, perceived ease of use, subjective norm, and perceived behavioural control. Overall, the findings give an extensive insight into the key determinants influencing consumers' intention to use ADRs and offer strategic policy recommendations to encourage the use of ADRs.

Factors affecting intention and attitude toward sidewalk autonomous delivery robots among online shoppers

ABSTRACT Sidewalk autonomous delivery robots (SADRs) are a new last-mile delivery service. The present study aims to identify the factors impacting the adoption of SADRs among Iranian online shoppers. To this end, based on consumer coproduction theory and technology readiness (TR), this paper combined individual factors, socialized factor, and risk factors. A total of 287 respondents were surveyed using an online questionnaire, and the partial least squares structural equation modelling (PLS-SEM) was employed to model the data. The modelling results revealed that attitude, innovativeness, and optimism had positive impacts on the intention of consumers to use delivery robots. Furthermore, optimism and environmental concern had a positive impact on attitude, while the need for human interaction, performance risk, and delivery risk had negative impacts. However, no significant relationship was found between innovativeness and attitude. The results of the present study provide important theoretical and practical suggestions to marketers and delivery service provides.

Consumer acceptance of autonomous delivery robots for last-mile delivery: Technological and health perspectives.

The unprecedented outbreak of the novel coronavirus has led to a great shift toward online retailing and accelerated the need for contactless delivery. This study investigates how technological and health belief factors influence consumer acceptance of autonomous delivery robots (ADRs). Anchored in four behavioral theories [i.e., technology acceptance model, health belief model, perceived value (VAL) theory and trust theory], a synthesized model is developed. A total of 500 valid responses were collected through an online questionnaire in Singapore, and structural equation modeling was conducted to examine the responses. The results revealed that perceived ease of use (EOU), perceived usefulness (UFN), perceived susceptibility (SUS), perceived severity (SEV), self-efficacy (SEL) and cues to action (CUE) have a positive and significant influence on consumers’ perceptions of the value of ADRs. The total effect analysis also showed that perceived VAL strongly affects consumer acceptance of ADRs. Academically, this study introduces both technological and health belief factors to explain consumer acceptance of ADRs. It also provides recommendations for policymakers and autonomous delivery robot developers on policy formulation, public communication, product design and infrastructure development.

Land Efficient Mobility: Evaluation of Autonomous Last Mile Delivery Concepts in London.

Land efficient last mile delivery concepts are key to reducing the traffic in cities and to minimising its environmental impact. This paper proposes a decision support method that evaluates the autonomous delivery concept and applies it to one year’s worth of real parcel delivery data in London. Deliveries to modular and fixed lockers with autonomous delivery vans and road-based autonomous lockers (RAL) and sidewalk autonomous delivery robots (SADRs) have been simulated. Various types of autonomous delivery van fleets, depot locations, customer modes of transport, parcel demand levels, parcel locker network densities and adjustment frequencies of modular lockers are considered. A routing and scheduling algorithm is used to optimise delivery tours and vehicle choice. The optimisation algorithm finds both the optimal number of collection and delivery points (CDPs) and the delivery concept (e.g., modular lockers, sidewalk autonomous delivery robot) depending on the customer mode chosen. The results show that modular lockers which are adjusted weekly are the best option for the current or higher parcel demand levels and road-autonomous parcel lockers (RAL-R) are the best option at the lowest parcel demand level.

Autonomous robot-driven deliveries: A review of recent developments and future directions

The adoption of autonomous delivery robots (ADRs) across various delivery applications has rapidly accelerated in recent years. This growth can be attributed to advancements in technology and legislation, exacerbated conventional delivery challenges, and pandemic-mediated need for contactless deliveries. A growing number of studies have investigated the various aspects pertaining to ADR-based delivery operations. Nevertheless, there is a lack of extensive literature review that holistically reports on various problems in ADR-based delivery operations, adopted methodologies, novel findings, and future needs. This paper aims to systematically survey the existing research on ADR-based delivery systems and associated operations. Foremost, we discuss various technology, design, and legal factors that impact ADR operations. Subsequently, we are the first to review the state-of-the-art research in ADR-based deliveries concerning the following areas—routing, infrastructure and fleet planning, factors affecting efficiency, technology acceptance, and social intelligence. Finally, we anticipate potential challenges from an operations management perspective, identify research gaps, and delineate future research opportunities for ADR-based deliveries.

New Mobility-Assist E-Grocery Delivery Network: a Load-Dependent Two-Echelon Vehicle Routing Problem With Mixed Vehicles

New forms of transport, such as autonomous delivery robots (ADRs), have attracted considerable attention in recent years for their potential use as green last-mile delivery alternatives because of their flexibility in some areas unreachable by van. However, their low efficiency, delivering a limited number of orders each trip, limits their application in the last mile. Also, the cost and emission impact of new forms of transport on the last-mile delivery network is still not clear. To address these issues, we developed a new two-echelon delivery system that combines traditional vans and ADRs, making use of their individual advantages to overcome their drawbacks and enhance efficiency in the deliveries. The objective of this study was to develop a new approach based on a metaheuristics methodology to minimize transport and emission costs through modeling and to solve the extension of two-echelon load-dependent vehicle routing problems with mixed vehicles (2E-LDVRP-MV). The complicated 2E-LDVRP-MV problem was formulated as a mixed-integer programming (MIP) model and solved efficiently with a cluster-based artificial immune algorithm (C-AIA) in which clustering was employed to allocate customers. We performed a set of numerical experiments with a solver and the proposed C-AIA heuristics. C-AIS highlights the operational implications of four parameters: ratios between load and the empty vehicle weight; vehicle types; emission levels; and customer densities. Our results provide a unique perspective on tactical planning for a sustainable urban logistics system, and this new two-echelon system could be applied in e-grocery last-mile delivery networks where vans and ADRs are combined.

Energy Demand Analysis and Powertrain Design of a High-Speed Delivery Robot Using Synthetic Driving Cycles

Last mile is known as the last leg of the delivery process, which is the most expensive and time-consuming part per kilometer. The most common last mile deliveries are postal packages, groceries and take away meals. Recently, there has been a growing interest and implementation of sidewalk autonomous delivery robots. These robots travel roughly at the speed of pedestrians (6 km/h). A high-speed (15 km/h) delivery robot design is proposed for reducing the time, energy and carbon footprint of deliveries. The preliminary design of the delivery robot powertrain is based on worst-case scenario analysis and Monte Carlo simulations with synthetic driving cycles. Synthetic driving cycles were used because there are no open data available of delivery robots. Thus, the procedure presented in this paper is a general approach for cases where there is no precedent application and/or no data are available. The synthetic driving cycles are based on start and end location of food-delivery services in Helsinki, Finland. Based on the simulations, the crucial factors contributing to energy demand and its variation were analyzed. Carbon footprint of the delivered package over distance of the design is compared to existing wheeled delivery robots and quadrupeds. The motivation of the work is that showcasing the energy savings of higher speed aids government officials in their decision-making regarding delivery robot regulations. As a result of the simulations, higher operation speed lowered the energy consumption by over 40%.

A mixed truck and robot delivery approach for the daily supply of customers

Innovative last-mile logistics solutions are needed to reduce delivery costs, traffic congestion, and pollution in cities. A promising concept in this context are truck-and-robot systems with robot depots, as they enable significant cost and traffic reduction compared to classic truck deliveries. The system relies on small autonomous delivery robots to cover the last meters to a customer. Existing concepts consider home deliveries by robots, while trucks are only used to transport robots and not for deliveries. This assumption disregards the fact that regular truck deliveries are still needed for some delivery requests, such as for the delivery of bulky items, or for customers who do not accept robots.Our research addresses this issue and proposes the first mixed truck and robot delivery concept with robot depots in which both robots and the delivery truck can visit customers. Our tailored solution approach is based on a General Variable Neighborhood Search that efficiently solves the routing problem and outperforms existing truck-and-robot routing algorithms (reducing the runtime by 37 to 94% in experiments). The numerical experiments show that this approach enables cost reductions of up to 43% compared to classical truck deliveries and up to 22% compared to a truck-and-robot system that does not allow deliveries by both truck and robots on the same tour. Further analyses reveal additional benefits of such mixed tours and the robustness of our approach for different problem settings.

Autonomous Delivery Robots: A Literature Review

Autonomous Delivery Robots: A Literature Review

Extending JSprit to solve electric vehicle routing problems with recharging

In this article, we present our adaptation of the Ruin-and-Recreate (R&R) algorithm to solve the electrical vehicle routing problem with time windows and multiple trips. We implement this adaptation in JSprit, an open-source vehicle routing problem solver. We implement the framework for a case study of Lyon, France. We assess the efficiency impact of adding charging constraints to a simulation of a fleet of autonomous delivery robots.

A Multi-Agent Control Architecture for a Rescue Robot

Cognitive architectures investigate the components and interactions neccessary for construction of an intelligent system. Despite much progress and theory, implementations of architectures are rare. This research presents a novel cognitive architecture grounded in the design of a control system for an autonomous rescue robot. Experiments are conducted in high-fidelity 3D simulation of a rescue environment based on NISTs RoboCup Rescue.

Cost‐optimal truck‐and‐robot routing for last‐mile delivery

AbstractDuring recent years, several companies have introduced small autonomous delivery robots and evidenced their technical applicability in field studies. However, a holistic planning framework for routing and utilizing these robots is still lacking. Current literature focuses mainly on logistical performance of delivery using autonomous robots, ignoring real world limitations, and does not assess the respective impact on total delivery costs. In contrast, this paper presents an approach to cost‐optimal routing of a truck‐and‐robot system for last‐mile deliveries with time windows, showing how to minimize the total costs of a delivery tour for a given number of available robots. Our solution algorithm is based on a combination of a neighborhood search with cost‐specific priority rules and search operators for the truck routing, while we provide and evaluate two alternatives to solve the robot scheduling subproblem: an exact and a heuristic approach. We show in numerical experiments that our approach is able to reduce last‐mile delivery costs significantly. Within a case study, the truck‐and‐robot concept reduces last‐mile costs by up to 68% compared to truck‐only delivery. Finally, we apply sensitivity analyses to provide managerial guidance on when truck‐and‐robot deliveries can efficiently be used in the delivery industry.

An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and delivery robots

Considering autonomous delivery robots in urban logistics has attracted a great deal of attention in recent years. In the meantime, new technology has led to new operational challenges, such as the routing and scheduling of vehicles and delivery robots together that are currently outside the logistics service providers’ capability. In this paper, a vehicle routing problem with time windows and delivery robots (VRPTWDR) as a variant of the classical VRP is studied. The investigated problem is concerned with the routing of a set of delivery vans equipped with a number of self-driving parcel delivery robots. To tackle the VRPTWDR, an Adaptive Large Neighborhood Search heuristic algorithm is developed. Experiments show the performance and effectiveness of the algorithm for solving the VRPTWDR, and provide insights on the use of self-driving parcel delivery robots as an alternative last mile service.

A mixed truck and robot delivery approach for the daily supply of customers

Innovative last-mile logistics solutions are needed to reduce delivery costs, traffic congestion, and pollution in cities. A promising concept in this context are truck-and-robot systems, as they enable significant cost and traffic reduction compared to classic truck deliveries. The system relies on small autonomous delivery robots to cover the last meters to a customer. Existing truck-and-robot concepts to date consider home deliveries by robots, while trucks are only used to transport robots and not for deliveries. This assumption disregards the fact that regular truck deliveries are still needed for some delivery requests, such as for the delivery of bulky items, or for customers who do not accept robots. Our research addresses this issue and proposes a mixed truck and robot delivery concept in which both robots and the delivery truck can visit customers. Our tailored solution approach is based on a General Variable Neighborhood Search that efficiently solves the routing problem and outperforms existing truck-and-robot routing algorithms. The numerical experiments show that this approach enables cost reductions of up to 43% compared to classical truck deliveries and up to 22% compared to a truck-and-robot system that does not allow deliveries by both truck and robots on the same tour. Further analyses reveal additional benefits of such mixed tours and the robustness of our approach for different problem settings.