Autonomous Rescue Robots Research Articles

Understanding the Interaction between Delivery Robots and Other Road and Sidewalk Users: A Study of User-generated Online Videos

The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2,067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces.

Enhancing mapping precision in autonomous delivery robots through tightly-coupled fusion of uncertainty-aware GPS and LiDAR odometry

Enhancing mapping precision in autonomous delivery robots through tightly-coupled fusion of uncertainty-aware GPS and LiDAR odometry

What Will the Delivery Robots Bring Us Tomorrow?

ABSTRACTAutonomous delivery robots offer a promising solution to the challenges of last mile logistics, a crucial topic in times of increasing logistics volume, environmental concern, and ongoing urbanization. This study investigates the acceptance of such robots in various application scenarios for last mile delivery in Germany. A first, quantitative, study investigating the overall acceptance of autonomous delivery robots for the primary predominant usage scenarios of meal and package delivery tested an adapted technology acceptance model via a structural equation model. A second, qualitative, study was conducted to better understand possible future use cases for these robots included semistandardized interviews with 14 individuals and employed a qualitative content structuring analysis method for data analysis. The results of Study 1 confirm that performance expectancy and effort expectancy influence the acceptance of delivery robots, with performance expectancy and behavioral intention being significantly higher for the package than for meal deliveries. However, in both cases, the average intention to use these robots only slightly exceeded the scale mean. Study 2 reveals that autonomous delivery robots are perceived as more convenient than existing alternatives in grocery and pharmaceutical delivery and return processing. The further application of emergency situations, such as illness or the risk of infection, also emerged from the data; even respondents who generally reject the idea of such robots would still use them in such exceptional situations. These results should be considered in the further development of autonomous delivery robot systems.

Dynamic pickup and delivery problem for autonomous delivery robots in an airport terminal

Autonomous delivery robot (ADR) operation for short-range delivery purposes is becoming an increasingly popular mode of service. Assigning orders to robots is critical in ensuring high quality in these automated delivery operations. This paper considers a dynamic pickup and delivery problem using autonomous robots (DPDP-AR), where a fleet of ADRs picks up desired items from stores and delivers them to customers. The arrival time of orders is uncertain, with a hard delivery deadline for each order. This study considered the battery consumption of ADRs, which has been less extensively covered in previous research, and devised a battery recharging strategy for the operation. To handle this stochasticity and dynamism in the problem, we developed a reassignment algorithm that reschedules previously assigned orders at the arrival of a new order. Additionally, as periods of high demand can be estimated, peak time management of the battery is proposed to enhance ADR utilization at peak periods. Computational experiments were performed on real-world ADR food delivery service instances in an airport terminal. Test instances on various demand scenarios demonstrated improvement in service quality when devised policies were used compared to current practice. We substantiated that the proposed algorithms found efficient solutions within short computation times, validating their applicability to real-world operations.

The effects of motivated consumer innovativeness on consumer acceptance of autonomous delivery robots

This research aimed to examine the impacts of motivated consumer innovativeness on customers’ acceptance of autonomous delivery robots (ADRs) using the privacy calculus theory (PCT) and motivated consumer innovativeness theory (MCI). It was suggested that this impact was mediated by perceived risks and perceived cooperativeness. Four hundred and fifty survey responses were gathered through a stratified random sampling approach to effectively represent the population of Singapore, ensuring the sample’s representativeness. The acquired data underwent analysis via structural equation modelling, uncovering that motivated consumer innovativeness (MCI) (i.e. functional MCI, hedonic MCI, cognitive MCI, and social MCI) influences the perceived (privacy) risks of using ADRs. In addition, the perceived risks influence the perceived cooperativeness of ADRs. Furthermore, the perceived risks and perceived cooperativeness both influence the consumers’ acceptance of ADRs. The combination of theories is highly effective in explaining consumers’ acceptance of ADRs, accounting for a substantial 78.3% of the variance. These findings highlight the significant impact of enhancing perceptions of ADR cooperativeness and transparent communication about privacy and safety on consumer acceptance. This, in turn, informs effective policy and strategic decisions for ADR development and deployment.

Robotic Delivery Worker in the Dark: Assessment of Perceived Safety from Sidewalk Autonomous Delivery Robots’ Lighting Colors

Sidewalk autonomous delivery robots (SADRs) share sidewalks with pedestrians and may affect their perceived safety. In outdoor nighttime environments, the color of the lights on SADRs serves as a noticeable form of communication that can influence human emotions. Therefore, this study investigated whether the perceived safety of SADRs varied with the colors of their lighting. In this study, an experiment (n = 30) was conducted where participants walked towards a robot from opposite directions to interact. The perceived safety of five different lighting colors (red, yellow, blue, green, and white) on SADRs was measured before and after the interaction using two perceived safety scales. The results showed significant differences in participants’ perceived safety for the robot’s red, green, blue, and yellow lighting before and after the interaction. Red lighting was rated the least perceived safe, while white and yellow lighting were rated the most perceived safe. Additionally, gender significantly influenced the perceived safety assessments, with females reporting lower perceived safety than males. These findings are valuable when designing SADRs that enhance pedestrians’ perceived safety, thereby facilitating their integration into broader environments in the future.

Autonomous delivery robots: a legal framework for infliction of game-theoretic small penalties on pedestrians

ABSTRACT Autonomous delivery robots (ADRs) must share and negotiate for public and private space with pedestrians. Game theory shows that this requires making credible threats of inflicting at least small harms onto members of the public, which requires new legal justification. To this end, we argue that ADRs could be considered as pedestrians under existing law. We propose ‘robot self-defence’ and right to property as the legal basis for inflicting the required ‘small penalties.’ We examine the liability framework when an ADR actually causes a collision either deliberately through actualising a credible collision threat or accidentally via errors in controlling intended non-contact threats. We explore challenges around privacy and data protection where the ADR collects and uses data to model and predict interactions. Together, this provides a framework for legal operation of ADRs, including the ability to inflict small harm onto members of the public when necessary in negotiations for space.

Towards a fundamental understanding on Microscopic Transportation Simulation of Autonomous Delivery Robots

A large number of studies were focused into the adoption of ADRs in the current transportation and logistics, safety in practical applications, and deployment of advanced robotic technology. Only a few contributed to the simulation of microscopic transportation for autonomous delivery robots. Many researchers emphasized the importance of overcoming modelling gaps and covering the scope of simulation as realistically as possible in order to obtain reliable results from the simulation. The versatility of the technology, the nature of the logistics and components of ADR in the real world, and other challenges present challenges to the transportation modeller when it comes to thinking about the possible representation of ADRs in the simulation environment. The gradual development of both computational capacity and underlying behavioural patterns in microscopic traffic simulators opens the door for researchers to develop realistic ADRs model in the microscopic transportation simulation to investigate road interaction and the resulting impacts. This review explored and organized the currently available technologies, operational methods, and simulation components that need to be considered when modelling ADRs in microscopic traffic simulation. Furthermore, this review provides a basic understanding for modellers to address the potential challenges to cope on recent technological developments in ADR modelling and simulation.

A Review of the Advances in Artificial Intelligence in Transportation System Development

In modern times, the rapid expansion of urban populations has intensified the urgency to optimize transportation systems, which has become an alarming issue in the face of urbanization and traffic congestion. This paper reviews the latest applications of Artificial Intelligence (AI) in the transport sector. It explores various AI methodologies, including Artificial Neural Networks (ANN), Genetic Algorithms (GA), Simulated Annealing (SA), Ant Colony Optimizer (ACO), Bee Colony Optimization (BCO), disruptive urban mobility, Fuzzy Logic Models (FLM), automated incident detection systems, and drones, which improve dynamic traffic management and route optimization. The study reveals that integrating these AI techniques with real-time data analytics improves traffic flow, automated incident management, and overall transportation efficiency. The results demonstrate that AI-driven systems, such as drones equipped with advanced sensors and AI algorithms, are increasingly capable of autonomous navigation, real-time monitoring, and predictive traffic management. These advancements in technologies, such as electric Vertical Take-off and Landing (eVTOL) aircraft, Hyperloop Transportation Technologies (HTT), Mobility-as-a-Service (MaaS) and autonomous delivery robots, contribute to smarter urban mobility solutions. However, it is important to focus on refining AI models for better performance, addressing challenges such as computational complexity and privacy concerns, and continuing to innovate in AI to improve the economic efficiency and reliability of transportation systems. Furthermore, to promote sustainability development in this sector, ethical considerations such as the protection of user information and the integration of the concepts of informed consent and human autonomy with community engagement programs should also be considered.

Autonomous delivery robots on the rise: How can I cut carbon footprint for restaurant food deliveries?

This study examines customers’ intentions to select an autonomous delivery robot (ADR) for off-premise restaurant delivery services, in response to the growing concerns about carbon emissions. Drawing upon Valence-Instrumentality-Expectancy theory, this study demonstrates how knowledge of transport emissions, awareness of carbon emissions, the perceived environmental performance of ADRs, and ADR usage intentions are interrelated. This study also explores differences between two groups: ADRs as high risk versus low risk. Using PLS-SEM and PLS-multigroup analysis with data from 418 participants, the study results show that more knowledge of transport emissions and higher awareness of carbon emissions are associated with more positive perceptions of ADRs' environmental performance and greater intentions to use ADRs. This study highlights the importance of ADRs’ reliability, which should be prioritized alongside environmental concerns. This study has theoretical contributions and practical implications, including policymaking, for stakeholders in the food delivery business.

DELIVERY AUTOMATION ROBOT USING ARDUINO MICROCONTROLLER

The Delivery Automation Robot project aims to develop a robust and efficient autonomous delivery robot for last-mile logistics operations. Built upon a foundation of sturdy hardware components including a four-wheel metal chassis and large wheels, the Delivery Automation Robot ensures stability and durability while navigating diverse terrains. Integrated with advanced control systems such as the L298n motor driver shield, NODEMCU, and Arduino Uno, the robot offers precise movement control and data processing capabilities. Equipped with essential sensors like the magnetometer HMC5883 and NEO 6m GPS module, the MDAR achieves accurate localization and navigation, crucial for optimizing delivery routes. Additionally, wireless communication facilitated by the HC06 Bluetooth module enables remote monitoring and control. Overall, the Delivery Automation Robot represents an innovative solution poised to revolutionize last-mile delivery processes, enhancing efficiency and reliability in logistics operations. Keywords: four-wheel chassis, L298n motor driver, NODEMCU, Arduino Uno, magnetometer HMC5883, NEO 6m GPS module, HC06 Bluetooth module.

Unravelling the last-mile conundrum: A comparative study of autonomous delivery robots, delivery bicycles, and light commercial vehicles in 14 varied European landscapes

In this study, the growing need for efficient delivery services in the expanding e-commerce sector is addressed, with a focus on real-life consumption data. A comprehensive modelling framework is proposed to evaluate the efficiency of various transportation modes, including Light Commercial Vehicles (LCVs), cargo bicycles, and Autonomous Delivery Robots (ADRs). Utilizing the Google API, delivery destinations are identified, origin-destination matrices are created, and routes are optimized using Google OR-Tools and a capacitated vehicle routing problem solver. The study's robustness is further enhanced by incorporating real-life consumption data, considering diverse European contexts, varying urban scales, traffic patterns, and topographical factors, thus assessing their impact on transportation efficiency. The findings reveal that ADRs are efficient in pedestrian-focused, traffic-limited areas, while bicycles are more effective in dense city centres. This research highlights the necessity of tailoring transportation mode choice to specific urban characteristics for optimal efficiency and consumer satisfaction.Overall, the present study offers valuable insights into optimizing delivery services in different urban settings, providing a significant model for improving last-mile delivery systems. It contributes to understanding how different transportation modes can be effectively integrated into urban logistics, addressing environmental sustainability, operational efficiency, and real-life consumer demands.

Sustainable last-mile distribution with autonomous delivery robots and public transportation

In recent years, the rapid growth of e-commerce and the need to make last-mile logistics more sustainable have stimulated the development of new distribution paradigms, based on air drones and autonomous delivery robots, which are less sensitive to traffic congestion. In this article we deal with a routing problem in which a fleet of autonomous delivery robots can travel not only on the road network but also on the public transportation system (or part of it) to extend their range of action with a given battery capacity. The problem entails building delivery robot routes synchronized with the rides of the public transportation lines, enabling robots to drop on/off public vehicles, where they use dedicated compartments, to reach customers that would be otherwise out of reach. To this purpose, we develop a tailored destroy-and-repair mechanism that, embedded into a neighborhood search algorithm, allows to effectively explore the feasibility region of large-scale instances. Extensive computational results on instances resembling the distribution of drugs to pharmacies in Rome (Italy) show that the proposed algorithmic mechanism allows to obtain a cost reduction up to about 7.5% with respect to a more traditional approach. Moreover, from a managerial point of view, our experiments show that autonomous delivery robots combined with public transportation can provide huge benefits in terms of costs and emissions reduction, when compared to both traditional and electric vans.

Sidewalk autonomous delivery robots for last-mile parcel delivery

The dynamics of e-commerce, combined with the growing number of users and the increasing volume of last-mile consignments, are driving current developments in logistics. As online commerce continues to grow, so does the need for active service providers to deliver courier, express, and parcel (CEP) shipments on time. Autonomous last-mile delivery is said to have the potential to transform the way we receive goods. As these autonomous delivery technologies continue to develop and become more widespread, it is possible to foresee a future where autonomous delivery vehicles seamlessly navigate our roads and sidewalks, bringing our online orders to our doorsteps faster and more efficiently than ever. After a general classification of sidewalk autonomous delivery robots (SADRs), this paper examines how far the development and implementation of these vehicles have progressed and which developments offer the greatest opportunities for ecological and economic use in the future.

Addressing the challenges of using autonomous robots for last-mile delivery

The importance of last-mile delivery (LMD) in today's logistics networks has increased recently due to the rising popularity of e-commerce and home delivery. Various LMD modes, such as drones and autonomous vehicles, have emerged to solve the problems encountered during LMD activities and to reduce human intervention in the process. The purpose of this paper is to determine the challenges that arise when using autonomous delivery robots (ADRs) in LMD. Throughout the research, the challenges have been revealed, categorized into six titles and their significance has been calculated via the intuitionistic fuzzy (IF) pivot pairwise relative criteria importance assessment (PIPRECIA) method in conjunction with PESTEL Analysis. IF-PIPRECIA is employed for the first time in the literature with PESTEL Analysis to evaluate the challenges for the implementation of ADRs in LMD. According to the proposed methodology results, challenges related to “Energy consumption,” “Battery life and energy management,” “Cybersecurity,” “Air pollution,” and “Uncertain return on investment” are the most critical factors to consider when implementing ADRs in LMD.

Uncertainty analysis of autonomous delivery robot operations for last-mile logistics in European cities

Although autonomous delivery robots (ADRs) are widely anticipated to significantly enhance the efficiency of last-mile logistics operations in dense urban environments in the coming years, their impact on logistics service providers’ supply chains has yet to be accurately assessed on a large scale. The primary objective of this article is to quantify the uncertainty in the cost of ADR operations as a function of the stochastic behavior of given input variables. First, ADR operations are modeled using the continuous approximation methodology. The mathematical formulations proposed in this article relate certain ADR and service area input parameters to provide an estimate of the carrier’s last-mile operating costs. An uncertainty analysis based on the Monte Carlo approach is then performed. The numerical results indicate that the implementation of a two-echelon delivery scheme with heavy-duty vehicles cooperating with ADRs through an urban logistics micro-hub would, on average, reduce last-mile operating costs by more than 10% in small and medium-sized European cities, given adequate operational conditions.

Zero emission freight transport and impacts on last mile delivery


 
 
 This literature review explores mainly the challenges and potential solutions in urban freight transportation, for last-mile logistics. The growing number of delivery vehicles in urban areas has led to problems such as traffic congestion, competition for parking spaces, noise, and air pollution. To address these issues, the paper focuses on the last mile problem in transportation logistics and the potential of disruptive innovations, a potential of disruptive innovations such as Unmanned Aerial Vehicles (UAVs) and Autonomous Delivery Robots (ADRs) as alternatives to traditional Internal Combustion Engine (ICE) vehicles. It highlights the drastic operational differences between aerial drones and ADRs and the need for changes in operational procedures to fully leverage these technologies. The study also discusses routing strategies and delivery routes, emphasizing the shift from one-route delivery to a point-to- point model with the advent of autonomous vehicle technologies. The paper concludes that while these technologies can reduce costs and emissions, their effectiveness depends on the specific urban setting and operational factors. 
 
 

A scenario-based feasibility analysis of autonomous robot deliveries

Autonomous robot deliveries have been considered as one of the ways to augment delivery resources in urban areas. Their high initial cost and strict operation regulations are often seen as the main barriers in this regard. However, a rise in demand of home deliveries and contactless deliveries in the wake of current COVID-19 pandemic has increased interest in autonomous delivery robots. This study presents a scenario-based feasibility analysis of autonomous deliveries using real data of two freight companies in Osaka, Japan. The minimum number of autonomous robots has been estimated in various delivery and cost scenarios and compared with the actual delivery operations. It was found that if current initial costs can be reduced due to mass production, autonomous robots with mothership vehicles can result in similar costs compared with truck deliveries along with the added advantage of low environmental impact. Furthermore, a simple index, counting the number of human-to-human interactions in delivery operations has been used to show the advantages of autonomous robot deliveries in case of pandemics.

Users Acceptance of Autonomous Delivery Robot Last Mile Delivery in Post-Pandemic Era: A Moderating Role of Perceived Trust

Users Acceptance of Autonomous Delivery Robot Last Mile Delivery in Post-Pandemic Era: A Moderating Role of Perceived Trust

Text Mining of South Korea YouTube Comments on Sidewalk Autonomous Delivery Robots

Text Mining of South Korea YouTube Comments on Sidewalk Autonomous Delivery Robots