Purpose The purpose of this paper is to provide details of the role of autonomous robots in the transportation and delivery sector of the logistics industry. Design/methodology/approach Following an introduction, this first discusses autonomous cargo shipping. It then considers autonomous road transport which is followed by details of short-distance and last-mile delivery robots. Finally, conclusions are drawn. Findings Transportation and delivery is a central element of the logistics industry but the sector faces operational, environmental and economic challenges. The industry is increasingly adopting automation and robotics, and this is now being extended to the transportation and delivery sector. Autonomous cargo ships have been demonstrated and have the potential to reduce costs and improve safety. The main barriers to widespread adoption are regulations and legislation. Autonomous road transport is gaining momentum and autonomous trucks are increasingly being deployed, particularly in the USA, where they operate on hub-to-hub routes. Short-distance and last-mile delivery is a rapidly growing sector and drones, autonomous vehicles and sidewalk robots are used widely to deliver foods, groceries and mail. Autonomous vehicles confer benefits which include improved safety, reduced environmental impact, lower fuel and labour costs and more rapid delivery and represent the most significant development in the drive to achieve full automation of the logistics industry. Originality/value This provides details of the rapidly growing role of robotics in the transportation and delivery sector of the logistics industry.

Freight-on-Transit operational problems with robot delivery: Genetic algorithm and Benders decomposition approaches

BackgroundAutonomous medication delivery robots can streamline hospital logistics. However, their feasibility under elevator congestion remains uncertain.ObjectiveTo evaluate the feasibility of medication delivery robots in a tertiary hospital and quantify how the elevator operating rate (EOR, %) affects delivery success, delay, and user experience.MethodsA prospective feasibility study was conducted in a tertiary hospital where a robot is used for delivering medicine. We analyzed 122 non-urgent missions from June 18–29, 2025, spanning weekdays and weekends. Data included the Elevator Operating Rate (EOR), passenger and cargo counts, Elevator Waiting Time, and Elevator Travel Time. The delivery outcomes were recorded, and a Monte Carlo simulation was used to model the failure probabilities under different congestion scenarios. The staff usability and workload were assessed using the System Usability Scale (SUS) and NASA Task Load Index (NASA-TLX).ResultsA Higher EOR was strongly associated with more delivery failures. Most failures resulted from physical obstruction by passengers or cargo. The data also confirmed that a high EOR coincided with greater elevator occupancy. Simulations incorporating space occupancy reproduced failure patterns similar to the in situ observations. An increased EOR also prolonged the delivery time. The staff reported relatively high usability, but the NASA-TLX scores indicated that frequent robot users felt greater time-related pressure, likely reflecting delays during congestion.ConclusionsAutonomous medication delivery is feasible. However, its performance is sensitive to elevator congestion. Effective deployment requires consideration of elevator usage rates, and robotic medication delivery should be scheduled when congestion is below critical thresholds to ensure reliability and minimize the staff burden.

ABSTRACT This study explores the integration of semiautonomous food delivery robots in contract foodservice, highlighting their impact on work and workplaces. Semiautonomous robots, a recent technology in hospitality, are rapidly becoming part of foodservice operations. There has been little attention given to the impact of robot adoption on the employee side of the hospitality work environment. To address this gap, this study explores the impact of the semiautonomous food delivery robots on employees. The study employed a qualitative approach using semi-structured interviews with employees at a U.S. university foodservice provider, a beta site for semiautonomous food delivery robots. Thematic analysis was conducted to identify key themes from the data. The study identified four key themes related to organizational support and management practices, technology usability and system integration, employee experience, and skill development, and workplace adaptations and team dynamics. These themes highlight shifts in work and workplaces. This study advances the growing body of knowledge on human-robot collaboration in the foodservice industry, extending research on semiautonomous delivery robots beyond their traditional focus on manufacturing and distribution settings.

Judge a robot by its cover? The impact of delivery purpose labels on willingness to help, perception, and evaluation of robot abuse

Walkability is a key component of sustainable urban development. In walkability studies, collecting detailed pedestrian infrastructure data remains challenging due to the high costs and limited scalability of traditional methods. Sidewalk delivery robots, increasingly deployed in urban environments, offer a promising solution to these limitations. This paper explores how these robots can serve as mobile data collection platforms, capturing sidewalk-level features related to walkability in a scalable, automated, and real-time manner. A sensor-equipped robot was deployed on a sidewalk network at KTH in Stockholm, completing 101 trips covering 900 segment records. From the collected data, different typologies of features are derived, including robot trip characteristics (e.g., speed, duration), sidewalk conditions (e.g., width, surface unevenness), and sidewalk utilization (e.g., pedestrian density). Their walkability-related implications were investigated with a series of analyses. The results demonstrate that pedestrian movement patterns are strongly influenced by sidewalk characteristics, with higher density, reduced width, and surface irregularity associated with slower and more variable trajectories. Notably, robot speed closely mirrors pedestrian behavior, highlighting its potential as a proxy for assessing pedestrian dynamics. The proposed framework enables continuous monitoring of sidewalk conditions and pedestrian behavior, contributing to the development of more walkable, inclusive, and responsive urban environments. • Sidewalk robots are used for real-time, scalable walkability data collection. • Framework enables continuous monitoring of sidewalk and pedestrian features. • Data reveals links between sidewalk design and pedestrian behavior. • Robot behavior strongly correlates with pedestrian movement patterns.

Last-mile logistics represents one of the greatest challenges in the supply chain, directly affecting operating costs and the end customer experience. The accelerated growth of e-commerce and increasing demands for fast, customized, and sustainable deliveries have intensified the need for innovative solutions. In this context, Artificial Intelligence (AI) emerges as a strategic technology, offering benefits in route optimization, cost reduction, customer experience enhancement, and environmental impact mitigation. This article aims to analyze the benefits and challenges of AI application in last-mile logistics, based on a literature review of studies published between 2023 and 2025. The findings indicate that intelligent algorithms, autonomous vehicles, delivery robots, and predictive systems are transforming operational efficiency and promoting sustainability. However, barriers such as high implementation costs, lack of specific regulation, data integration issues, and organizational resistance still hinder widespread adoption. The study concludes that AI integration into last-mile logistics is a driver of business competitiveness, provided it is accompanied by investments in infrastructure, workforce training, and regulatory advancements.

In response to the increasing demand for door-to-door services and the complexities of modern last-mile delivery models, this study investigates a two-echelon vehicle routing problem for last-mile delivery based on a multi-fleet framework. This framework includes electric vehicles (EVs) to transport robots and cargo, delivery robots (DRs) for terminal deliveries, and vans to recover robots, which is also referred to as an EV-DR-van system. We propose a mathematical model to address the two-echelon electric vehicle routing problem, considering time window constraints, the utilization of delivery robots, and a partial charging strategy, named 2E-EVRPTWDR-PC. We employ the CPLEX solver to obtain optimal solutions for small-scale instances. For larger instances, we design an adaptive large-neighborhood search algorithm (ALNS) to effectively explore and adapt to the complex solution space, producing high-quality results. The data for this research are generated by modifying Solomon’s dataset. Our experimental results confirm the proposed model’s validity and the algorithm’s effectiveness. Comparative analyses indicate that using robots for final deliveries can reduce overall time costs by approximately 20% compared to traditional delivery methods. This research has significant implications for optimizing last-mile delivery systems and improving delivery efficiency.

Robot-aided electric vehicle routing problem with lockers and prime customers prioritization

European policymakers chose the systematic funding of smart city initiatives to incentivize and accelerate innovation and sustainability transitions. To ensure these initiatives’ broader effectiveness, smart city replication has been incorporated in funding calls for research projects as a policy instrument for innovation diffusion, information dissemination and mutual learning. With a growing theoretical and empirical base for these replication activities, there is an increased awareness that integrating and transferring new ideas and solutions into the urban context requires a holistic perspective and includes various endogenous and exogenous influencing factors. This article proposes a systemic view by presenting a method for analysing the replication environment for autonomous delivery robots based on a causal loop diagram. The method is applied conceptually to a district in Munich. The developed approach, which is called Replication Causal Loop Diagram, serves as an analytical tool to generate and provide relevant contextual knowledge and information about the replication environment to facilitate the operational planning and implementation of replicable initiatives, solutions, and practices. In further development steps and in particular settings, the approach can also be a valuable addition to the replication portfolio for stakeholder engagement and consensus building.

The study examines the factors influencing consumers’ intentions to use autonomous outdoor food delivery robots (OFDR) and forward company generated social media content by integrating the Technology Acceptance Model (TAM) with innovativeness of motivated consumers. Based on 403 valid responses from Malaysia, all hypotheses were supported except those related to functional and hedonic innovativeness, which showed no moderating effects between perceived usefulness and attitude. Social innovativeness did not moderate the relationship between perceived ease of use and attitude. Theoretically, the study extends understanding of digital consumer behavior in the the service domain by introducing an integrated model that enhances TAM with dimensions of consumer innovativeness, namely functional, hedonic, cognitive, and social. Practically, it provides insights for policmakers and businesses in promoting OFDR adoption through strategies that foster positive attitude, perceived usefulness, and ease of use, reducing labor costs and carbon emissions, as well as facilitating last-mile delivery in a developing nation.

Urban delivery demand continues to rise, intensifying last-mile logistics challenges and accelerating the transition from manual delivery to autonomous delivery robots (ADRs). This study investigates the behavioral mechanisms underlying consumers’ migration toward ADRs. Grounded in the socio-technical systems perspective, we integrate the Push–Pull–Mooring (PPM) model with Social Cognitive Theory (SCT) to explain how technological and social stimuli shape switching and continuance intentions through cognitive and emotional pathways. Survey data from 786 Chinese consumers, analyzed using second-order structural equation modeling, support the proposed framework. The results indicate that dissatisfaction with manual delivery (push) and perceived benefits of ADRs (pull) significantly enhance both switching and continuance intentions. Outcome expectancy positively predicts switching intention but negatively predicts continuance intention. Technophobia reduces switching intention but does not significantly influence continuance. Moreover, social norms moderate key relationships, highlighting the role of external social influence in technology transition. This study extends PPM research into the smart logistics context, introduces socio-cognitive mechanisms into technology switching analysis, and conceptually distinguishes switching and continuance intentions as separate constructs. The findings offer practical guidance for ADR developers and policymakers by emphasizing strategies to reduce emotional resistance, enhance social endorsement, and promote the sustainable adoption of autonomous delivery technologies.

Abstract Humans are responsible for the majority of deliveries in India. When compared to other delivery systems,robotic systems offer more advantages because they are a large transportation medium capable of transporting huge items across long distances. Automation, such as driver-less robots, is a result of technological growth. This proposed model depicts an automatic robot system that travels from point A to point B without the need for human involvement. The sensors are used to identify obstacles. If there are any obstructions in the path, the robot will stop and restart its travel once the obstacle has been cleared. The basic premise of a self-driving system is to have a robot that can transport physical objects from one area to another. A self-contained delivery robot is a robot that makes deliveries. The self-driving robots uses artificial intelligence technology and stops at the destination after following the correct path. The robot is used for safe delivery of the packets. Keywords: Retinopathy, Segmentation, Image Processing

This study empirically investigates the deployment of Internet of Things (IoT)-enabled autonomous food delivery robots as technology-mediated service agents in the restaurant sector of Goa, India. Integrating perspectives from service marketing, hospitality management, and smart service systems, the research examines the dual impact of robotic food delivery on operational efficiency and customer experience. A quasi-experimental research design was adopted in simulated restaurant environments, comparing robotic delivery with conventional human-based service encounters. Key performance indicators included delivery time, service accuracy, staff intervention frequency, and customer satisfaction measures. The findings reveal that IoT-enabled food delivery robots significantly enhance service speed, reliability, and process consistency, while also generating positive customer perceptions related to innovativeness, convenience, and service professionalism. However, challenges persist in terms of system reliability, battery endurance, and the perceived absence of human warmth in service encounters. The study contributes to interdisciplinary literature by conceptualizing service robots as frontline service interfaces and offers actionable insights for hospitality managers seeking technology-driven service differentiation in tourism-intensive regions

This paper presents the design, modeling, and feasibility study of a magnetic resonance (MR)-conditional steerable neurosurgical robot for minimally invasive intratumoral delivery of therapeutic agents. Immunotherapy is an emerging brain tumor treatment technique but faces challenges due to low trafficking with systemic infusions, particularly in the case of large tumors. To address this limitation, we have developed a novel robotic system capable of delivering therapeutic agents throughout the entire volume of the brain tumor. The robot consists of a straight, rigid outer tube and a flexible inner tube that can navigate along curved paths and articulate in 3D space. A custom-designed injection mechanism consisting of syringes and hydraulic transmission is integrated into the robotic system. A non-magnetic actuation system enables robot navigation to various locations within the tumor. Therefore, by delivering therapeutic agents to individual target locations, the overall trafficking and efficiency can be potentially improved. Characterization-based control experiments yielded a curvature control error of 2.6 ± 1.8% and a relative tip tracking error of 4.2 ± 3.9%, demonstrating the high accuracy of our control strategy. A phantom study demonstrated a significant improvement of the tumor coverage ratio made by the robotic needle compared to the straight needle (73% vs. 29%). An MRI-guided manipulation study showed an acceptable decrease in the signal-to-noise ratio (up to 1.41%) when the robot is manipulated in the water phantom. All these studies synergistically validated the feasibility of our new approach of robotically steerable, MRI-guided therapeutic delivery.

The integration of robotics into service environments is transforming how labor-intensive tasks are managed, particularly during peak hours with staff shortages and long wait times. This research presents a fully autonomous, modular food-delivery robot designed to enhance operational efficiency and improve service experience. The system combines artificial intelligence, facial recognition, smartphone-based order management, Arduino, ESP32, ESP32-CAM, and Python to navigate indoor environments and deliver food directly to recipients, supported by a secure handover mechanism. Experimental results indicate that the robot performs waiter-like delivery reliably, maintaining mobility and structural integrity across various surfaces by using lightweight materials and motors that have been optimized. Through the use of a motion coordination algorithm, responsive navigation can be achieved, while a simple user interface can be operated by anyone with minimal training. According to these results, automation reduces the need for manual labor, increases the speed of service, and ensures consistency in the delivery process. Additionally, the system provides a practical framework for future research and potential applications beyond food delivery, such as surveillance, environmental monitoring, and disaster response. Future work will focus on scaling for real-world deployment and integration advanced AI navigation to enhance autonomy, adaptability, and overall operational performance.

Cost-Efficient Semantic-Aided Outdoor Localization for Delivery Robots Without Prior Mapping

The convergence of persistent Augmented Reality (AR), robotics, and biometric analytics is creating a new technological paradigm where the digital and physical worlds are inextricably intertwined. This integration, while promising, introduces profound ethical risks that traditional governance models fail to address. This paper examines the complex challenges of data governance in this new era. Using a multi-method approach that integrates a systematic literature review, four purposive case studies (algorithmic recruitment, AR law enforcement, consumer wearables, and autonomous delivery robots), and thematic analysis, this research investigates four primary risk domains: (1) deep inferential threats from biometric and behavioural data, (2) cognitive manipulation and pervasive surveillance, (3) the "bystander problem" of non-consensual data capture, and (4) the diffusion of accountability in complex autonomous systems. Findings reveal systemic vulnerabilities, including "ambient biometric surveillance," "bystander invisibility," and "distributed responsibility," demonstrating the inadequacy of existing individualistic consent frameworks. The paper concludes by proposing a dual-pronged governance framework. This framework combines technical safeguards, such as dynamic consent architectures and mandatory AI Impact Assessments (AI-IAs), with policy innovations, including new legal categories for bystander data and multi-stakeholder co-regulatory oversight, to steer technological development toward a human-centric, rights-respecting future.

This project presents a Smart Delivery Robot designed for automated indoor delivery in multi-floor buildings.The system aims to reduce human effort and improve delivery efficiency through autonomous operation.It uses an embedded controller integrated with motors, sensors, wireless communication, and a camera for live video streaming.The robot navigates indoor environments, avoids obstacles, and provides real-time visual monitoring.A lift panel press mechanism enables independent elevator operation, allowing seamless movement between floors.Experimental results show reliable navigation, effective obstacle avoidance, and stable live video transmission.The proposed system supports contactless delivery and is suitable for applications in hospitals, offices, and smart buildings.

In meal delivery robot path planning, enabling the robot to find an optimal path that avoids obstacles within its workspace is a crucial step. Usually, the traditional ant colony optimization (ACO) suffers from slow convergence and blind search behavior in path planning, lacking dynamic obstacle avoidance functionality. Meanwhile, the dynamic window approach (DWA) tends to become entrapped in local optima during local path planning. It is therefore proposed that a hybrid path planning algorithm be developed, based on an improved IACO and DWA algorithm. To address issues such as aimless search, slow convergence speed, and low path smoothness in ACO, the concept of gravity from gravity search algorithms is introduced to direct the search. The acceleration of convergence is achieved through the implementation of path sorting and the administration of additional pheromone to superior paths in pheromone updates. The transition paths are optimized to address the issue of excessive path transitions in ACO, resulting in smoother paths. The key nodes of the obtained globally optimal path are used as local target points, serving as multiple target points for DWA operation to enable dynamic obstacle avoidance. Simulation results indicate that compared to the ACO, the IACO reduces path length by up to 30.03% and decreases path turns by up to 71.43% in four different static maps. In other static comparison experiments, the IACO demonstrated superior performance compared to the other tested algorithms. In dynamic experiments, the proposed fusion algorithm can plan smooth paths that successfully avoid both static and dynamic obstacles.