Life-cycle analysis of last-mile parcel delivery using autonomous delivery robots

As autonomous driving technology is getting more and more mature today, autonomous delivery companies like Starship, Marble, and Nuro has been making progress in the tests of their autonomous delivery robots. While simulations and simulators are very important for the final product landing of the autonomous delivery robots since the autonomous delivery robots need to navigate on the sidewalk, campus, and other urban scenarios, where the simulations can avoid real damage to pedestrians and properties in the real world caused by any algorithm failures and programming errors and thus accelerate the whole developing procedure and cut down the cost. In this case, this study proposes an open-source simulator based on our autonomous delivery robot ZebraT to accelerate the research on autonomous delivery. The simulator developing procedure is illustrated step by step. What is more, the applications on the simulator that we are working on are also introduced, which includes autonomous navigation in the simulated urban environment, cooperation between an autonomous vehicle and an autonomous delivery robot, and reinforcement learning practice on the task training in the simulator. We have published the proposed simulator in Github.

This literature review investigates how self-driving autonomous delivery robots (ADRs) impact last-mile deliveries, add value to the logistics and transport industry, and contribute to creating competitive business models. Autonomous vehicles are still a developing technology and ADRs could possibly be one of the solutions to the last-mile problem, in particular in cities and for urban freight with an increasing number of parcels to deliver. Last-mile delivery is also changing as e-commerce and more demanding customers emerge. Such development, however, faces challenges regarding infrastructure, externalities such as CO2 emissions, and shorter delivery-time requirements. This review, focused on ADRs, reveals four major themes (operations, infrastructure, regulations, and acceptance) through which we explain the barriers and benefits of using ADRs for last-mile deliveries. The review shows that the operations of ADRs can impact last-mile deliveries by lowering costs, optimising the use of time, and reducing externalities. The review also shows that the foundation of last-mile infrastructure would have to change if ADRs are to be used to a greater extent. Regulations for ADRs are still not yet in place, which makes the market somewhat confused. The acceptance of ADRs in society is another challenge because the innovation of ADRs is still new and unfamiliar. Altogether, the use of ADRs for last-mile deliveries shows great potential, based on the promising results of the articles reviewed. However, most studies on ADRs have been theoretical in nature, such as models, which highlights the need for real-world case studies and implementations.

The technology for autonomous driving is developing rapidly. One application of autonomous driving is the Autonomous Delivery Robots (ADR). The ADR has gained a lot of attention in both the academia and industry. However, most of the extant studies about the ADR focus on the technical side. There is a lack of research on the users' perspective about this new technology. This study applies topic modeling and sentiment analyses to summarize the comments in Weibo about the ADR. The results show that there are both positive and negative sentiments regarding the ADR. The contribution of this study is that the results can help researchers understand the users' concerns about ADR. The negative sentiments can provide guidance to the ADR developers to improve the design of the robots and to allay the users' concerns.

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

Purpose This study investigates the factors influencing delivery workers’ willingness to collaborate with autonomous delivery robots (ADRs). As ADRs become more prevalent in Logistics 4.0 environments, understanding human–technology collaboration is critical for supporting both operational efficiency and decent work. Design/methodology/approach We draw from the technology acceptance model (TAM) and service robot acceptance model (sRAM) to develop a model and examine the impact of functional, social and relational factors on delivery workers’ willingness to collaborate with ADRs. A field survey with a sample size of 483 and an online survey with a sample size of 292 were conducted to test the relationships of interest. Findings The results indicate that perceived usefulness, social influence and anthropomorphism have a positive influence on willingness to collaborate with ADRs, with procedural fairness acting as a significant mediator. While the overall model holds across both samples, differences in the strength of relationships suggest that cultural context shapes how employees perceive and respond to ADRs. Originality/value This study contributes to the literature by extending TAM and sRAM to the logistics sector and providing a cross-cultural perspective on employee–ADR collaboration. It addresses a critical gap in logistics and supply chain research, providing practical approaches to technology integration that support decent work in the Logistics 4.0 era.

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

In this research paper, we present autonomous delivery robots and their contribution during the pandemic. The paper introduces two autonomous delivery robots in more detail. These are Starship and Scout. Starship is under the company Starship Technologies, and Scout is part of Amazon. Both robots have made a significant contribution to the field of autonomous delivery robots. During the pandemic, the need for these robots was further highlighted. In the future, these robots will appear everywhere, and there will be even bigger upgrades on them - this is just the beginning of the era of autonomous robots as we see them now. We found out that both robots are in broad use on many fields. Scout is about two times heavier, four times faster in speed and can carry 27% more freight in comparison to the Starship robot.

Acceptance of autonomous delivery robots in urban cities

Autonomous delivery robots and their potential impacts on urban freight energy consumption and emissions

The integration of robots into daily life is becoming increasingly common, with Autonomous Delivery Robots (ADR) gaining prominence as an eco-friendly delivery option. Understanding how individuals perceive ADR when encountered unintentionally is crucial, especially given the important role the public has concerning sharing public space which form the research objective of this study. Study outcome becomes relevant during planning and integration of ADR, facilitating seamless operation. Employing a mixed-method approach, including questionnaires and interviews, the analysis identified four themes related to pre-conception, attitude, robot design and behaviour, and traffic system integration. Study results highlight the perceived usefulness and generally accepting attitude toward the idea of ADR. While the majority of the public perceives sharing space with ADR positive, special attention is needed concerning the area of operation and considering the factors speed and size. Different perceptions of the ADR behaviour could be noted with the ADR adapting to participants being considered a necessity.

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.

Since the advent of the fourth industrial revolution, the autonomous delivery robot industry has been growing explosively. Existing autonomous delivery robots exhibit limited high-speed mobility performance owing to inertial and centrifugal forces caused by the high center of mass. Therefore, this study proposed an autonomous delivery robot control method based on model predictive control (MPC) to facilitate the high-speed mobility of these robots via Gazebo simulations.In this study, a simulation was conducted based on HuboQ, which was manufactured as part of the research on developing basic technology for robots that can move at high speed. Regarding the HuboQ motion plan, discrete MPC was applied to the cart inverted pendulum model. To verify the stability of the target robot and a delivery item, a 1 kg cylindrical delivery item was placed on the robot in the simulation. Subsequently, the robot moved according to a rounded square trajectory and an 8-shape trajectory. The simulation results indicated that the delivery item moved by 9 mm in the x-axis direction and by 10 mm in the y-axis direction at maximum. Based on the simulation results, it was verified that the delivery item was stably controlled even when the robot moved at a high speed of 11km/h or higher.A demonstration of our method can be seen in the video: https://youtu.be/_E_AzuI2_pE.

This paper introduces the concept of motion planning of delivery robot in an autonomous driving mode using an inverted pendulum model that can effectively control disturbance. The inverted pendulum model exhibits the non-minimum phase characteristic caused by the right half-plane zero. An effective method of reducing this characteristic is examined. A motion platform with 3-degree-of-freedom motion and a touch sensor are installed on a wheeled omnidirectional mobile platform. A steel ball is placed on the touch sensor and controlled to be located at the center. As the autonomous delivery robot moves, the steel ball is subjected to various disturbances and goes off the center. The influence of disturbance can be predicted by measuring the distance the steel ball moves away from the center. In this paper, linear quadratic regulator, preview control, and model predictive control are applied to the inverted pendulum model for motion planning, and thus the reduction of the non-minimum phase characteristic can be comparatively analyzed via simulation. The decrease in the disturbance is experimentally compared according to motion planning. Consequently, this paper proposes an effective motion planning method for an autonomous delivery robot with non-minimum phase characteristic.

Evaluating public acceptance of autonomous delivery robots during COVID-19 pandemic

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