Life Cycle Greenhouse Gas Emissions for Last-Mile Parcel Delivery by Automated Vehicles and Robots

Path Planning of a Mobile Delivery Robot Operating in a Multi-Story Building Based on a Predefined Navigation Tree

Planning the path of a mobile robot that must transport and deliver small packages inside a multi-story building is a problem that requires a combination of spatial and operational information, such as the location of origin and destination points and how to interact with elevators. This paper presents a solution to this problem, which has been formulated under the following assumptions: (1) the map of the building's floors is available; (2) the position of all origin and destination points is known; (3) the mobile robot has sensors to self-localize on the floors; (4) the building is equipped with remotely controlled elevators; and (5) all doors expected in a delivery route will be open. We start by defining a static navigation tree describing the weighted paths in a multi-story building. We then proceed to describe how this navigation tree can be used to plan the route of a mobile robot and estimate the total length of any delivery route using Dijkstra's algorithm. Finally, we show simulated routing results that demonstrate the effectiveness of this proposal when applied to an autonomous delivery robot operating in a multi-story building.

Integrating sustainability in the design process of urban service robots

The concept of sustainability and sustainable development has been well discussed and was subject to many conferences of the EU and UN resulting in agendas, goals, and resolutions. Yet, literature shows that the three dimensions of sustainability (ecological, social, and economic) are unevenly accounted for in the design of mechatronic products. The stated reasons range from a lack or inapplicability of tools for integration into the design process, models for simulation, and impact analyses to necessary changes in policy and social behavior. The influence designers have on the sustainability of a product lies mostly in the early design phases of the development process, such as requirements engineering and concept evaluation. Currently, these concepts emerge mostly from performance-based requirements rather than sustainability impact-based requirements, which are also true for service robots in urban environments. So far, the main focus of research in this innovative and growing product branch lies in performance in perception, navigation, and interaction. This paper sets its focus on integrating all three dimensions of sustainability into the design process. Therefore, we describe the development of an urban service robot supporting municipal waste management in the city of Berlin. It is the set goal for the robot to increase the service and support the employees while reducing emissions. For that, we make use of a product development process (PDP) and its adaptable nature to build a specific development process suited to include the three dimensions of sustainability during the requirements engineering and evaluation activities. Herein, we show how established design methods like the life cycle assessment or life cycle costing can be applied to the development of urban service robots and which aspects are underrepresented. Especially, the social dimension required us to look beyond standardized methods in the field of mechanical engineering. Based on our findings, we introduce a new activity to the development process that we call preliminary social assessment in order to incorporate social aspects in the early design phase.

ParcEMon: IoT Platform for Real-Time Parcel Level Last-Mile Delivery Greenhouse Gas Emissions Reporting and Management

Transport is Australia's third-largest source of greenhouse gases accounting for around 17% of emissions. In recent times, and particularly as a result of the global pandemic, the rapid growth within the e-commerce sector has contributed to last-mile delivery becoming one of the main emission sources. Delivery vehicles operating at the last-mile travel long routes to deliver to customers an array of consignment parcels in varying numbers and weights, and therefore these vehicles play a major role in increasing emissions and air pollutants. The work reported in this paper aims to address these challenges by developing an IoT platform to measure and report on real-world last-mile delivery emissions. Such evaluations help to understand the factors contributing to freight emissions so that appropriate mitigation measures are implemented. Unlike previous research that was completed in controlled laboratory settings, the data collected in this research were from a delivery vehicle under real-world traffic and driving conditions. The IoT platform was tested to provide contextualised reporting by taking into account three main contexts including vehicle, environment and driving behaviours. This approach to data collection enabled the analysis of parcel level emissions and correlation of the vehicle characteristics, road conditions, ambient temperature and other environmental factors and driving behaviour that have an impact on emissions. The raw data collected from the sensors were analysed in real-time in the IoT platform, and the results showed a trade-off between parcel weight and total distance travelled which must be considered when selecting the best delivery order for reducing emissions. Overall, the study demonstrated the feasibility of the IoT platform in collecting the desired levels of data and providing detailed analysis of emissions at the parcel level. This type of micro-level understanding provides an important knowledge base for the enhancement of delivery processes and reduction of last-mile delivery emissions.

A Review of COVID-19-Related Literature on Freight Transport: Impacts, Mitigation Strategies, Recovery Measures, and Future Research Directions

The COVID-19 pandemic has caused significant disruptions in the freight transport sector. The number of studies on the impact of COVID-19 on freight transport and possible mitigation strategies are growing. However, a systematic and comprehensive review highlighting the research themes, main findings, research methods, and future research directions of these studies remains scarce. Therefore, this study presents a mixed review comprising scientometric and systematic reviews to cover these research gaps. Results show that 68 studies have been published on this topic since the beginning of 2020 and that they cover three main themes: the impacts of COVID-19 on freight transport, mitigation strategies, and recovery during and after COVID-19. In addition, we describe the research methods, main findings, and possible research directions in each of them. Thus, the findings of our work present both theoretical and practical analyses of COVID-19-related research on freight transport and provide important future research directions in this domain.

Carbon Footprint of Alternative Grocery Shopping and Transportation Options from Retail Distribution Centers to Customer

The COVID-19 pandemic has accelerated the growth of e-commerce and automated warehouses, vehicles, and robots and has created new options for grocery supply chains. We report and compare the greenhouse gas (GHG) emissions for a 36-item grocery basket transported along 72 unique paths from a centralized warehouse to the customer, including impacts of micro-fulfillment centers, refrigeration, vehicle automation, and last-mile transportation. Our base case is in-store shopping with last-mile transportation using an internal combustion engine (ICE) SUV (6.0 kg CO₂e). The results indicate that emissions reductions could be achieved by e-commerce with micro-fulfillment centers (16-54%), customer vehicle electrification (18-42%), or grocery delivery (22-65%) compared to the base case. In-store shopping with an ICE pick-up truck has the highest emissions of all paths investigated (6.9 kg CO₂e) while delivery using a sidewalk automated robot has the least (1.0 kg CO₂e). Shopping frequency is an important factor for households to consider, e.g. halving shopping frequency can reduce GHG emissions by 44%. Trip chaining also offers an opportunity to reduce emissions with approximately 50% savings compared to the base case. Opportunities for grocers and households to reduce grocery supply chain carbon footprints are identified and discussed.