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What is the current open research field in last mile delivery robot?

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The current open research field in last-mile delivery robots focuses on enhancing efficiency and automation through advanced technologies like SLAM, deep learning, and AI . Researchers are developing models like RADP-1 and RADP-2 to optimize delivery routes based on customer densities and operational uncertainties, aiming to minimize delivery latency . Additionally, there is a growing interest in customer-centric and safety-conscious systems that cater to real-world challenges, preferred time schedules, and pedestrian safety, utilizing AI-assisted autonomous delivery robots for end-to-end automation and optimization of logistic processes . These advancements aim to address the time-consuming and costly nature of last-mile delivery while considering environmental concerns and the surge in e-commerce sales .

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Book Chapter•DOI Robot-Assisted Delivery Problems and Their Exact Solutions Abdullahi Mohammed Jingi, Xin Zhu Yang - Show less +1 more 01 Jan 2023	The current open research field in last-mile delivery robots focuses on optimizing efficiency in high-density customer areas using MILP models like RADP-1 and RADP-2.
Journal Article•DOI Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots Areg Karapetyan, Arjun Sharma, Murad Mebrahtu, Mustofa Basri, Mohamed Nagy, Majid Khonji, Jorge Dias - Show less +6 more 28 May 2023-arXiv.org	The current open research field in last-mile delivery robots focuses on AI-augmented autonomous systems for efficient, safe, and customer-centric small urban community logistics.
Open access•Posted Content•DOI Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots 28 May 2023	The current open research field in last-mile delivery robots focuses on AI-augmented self-driving robots for safe and autonomous deliveries in small urban communities, optimizing routes and ensuring customer satisfaction.
Journal Article•DOI Design and Implementation of the Self-Driving Based Last Mile Delivery System Using SLAM and OCR G. H. Park, Seong-Yun Cho, Jong-gun Park, Hyunseok Yang, Taewoon Kim - Show less +4 more 30 Nov 2022-The Journal of Korean Institute of Communications and Information Sciences	The current open research field in last mile delivery robots focuses on implementing SLAM, OCR, and deep learning for autonomous delivery to enhance efficiency and reduce reliance on human labor.
Open access•Book Chapter•DOI Development of a ROS-Based Architecture for Intelligent Autonomous on Demand Last Mile Delivery Georg Novotny, Walter Morales-Alvarez, Nikita Smirnov, Cristina Olaverri-Monreal - Show less +3 more 29 May 2023-Lecture Notes in Computer Science	Route optimization methods and precise mapping for autonomous last mile delivery robots are current open research fields in the domain, as highlighted in the paper.

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What are the research gaps of the application of Last Mile Delivery using Robots?5 answersThe research gaps in the application of Last Mile Delivery using Robots include the need for a comprehensive assessment comparing UAVs and SUGVs in urban parcel delivery. Additionally, challenges exist in estimating robot travel time and optimizing robot routing for indoor last-mile delivery. There is a gap in the development of autonomous delivery solutions to address urban challenges in last-mile logistics operations, considering factors like increased congestion, pollution, and customer service expectations. Furthermore, optimizing delivery routes for last-mile logistics services remains a challenge due to discrepancies between optimized routes and preferred routes by practitioners, highlighting the need for a novel hierarchical route optimizer combining optimization and machine learning approaches.

In what ways the technology innovation, like drone system, autonomous delivery, would change the future of last-mile delivery?4 answersThe technology innovation, such as drone systems and autonomous delivery, is expected to bring significant changes to the future of last-mile delivery. These advancements offer potential solutions to improve efficiency, reduce delivery time, and decrease CO2 emissions and energy consumption in logistics operations. The implementation of autonomous delivery solutions can help tackle urban challenges related to last-mile logistics, such as increased congestion, traffic, air pollution, and driver shortages. By adopting AI-assisted autonomous delivery robots and unmanned aerial vehicles (UAVs), last-mile delivery can be automated and optimized, considering real-world operational uncertainties, customer preferences, and safety. The use of optimization models and algorithms can help determine optimal flying routes and schedules for UAVs, leading to near-optimal dispatch plans within reasonable computation time. However, there are still limitations and challenges that need to be addressed, such as maintenance, recharging, weather conditions, legislation, and implementation strategies.

What are the difficulties in last mile delivery?5 answersThe difficulties in last mile delivery include the complexity of transporting cargo to the recipient's door, inefficient routes, and the inability to optimize routes due to customer locations. Other challenges include traffic congestion, air and noise pollution, and carbon emissions. There are also gaps between optimized routes and the routes preferred by practitioners, which can be attributed to the difference between what is being optimized and what practitioners actually care about. Additionally, unstructured addresses pose a problem, particularly in developing countries, as they are difficult to read and locate, leading to increased delivery times and costs. These difficulties highlight the need for innovative solutions such as electric vehicles, cargo bikes, and drones, as well as partnerships between stakeholders to achieve sustainable last-mile delivery.

What are the main sustainability challenges of last-mile delivery?5 answersThe main sustainability challenges of last-mile delivery include traffic congestion, air and noise pollution, carbon emissions, and high costs. These challenges have a significant impact on the efficiency and environmental impact of the supply chain. To address these challenges, researchers have explored various sustainable last-mile delivery solutions, such as electric vehicles, cargo bikes, and drones. These innovative solutions have the potential to reduce environmental impact and improve the efficiency of last-mile delivery while enhancing social equity. Additionally, partnerships between different stakeholders, including governments, logistics companies, and consumers, are crucial for the development of sustainable last-mile delivery solutions. However, the economic feasibility and scalability of these solutions require further research. Overall, sustainable last-mile delivery solutions are essential for achieving sustainable supply chains and reducing the negative environmental and social impacts of last-mile delivery.

What are the environmental effects of last mile delivery?5 answersLast mile delivery has significant environmental effects, including traffic congestion, air and noise pollution, carbon emissions, and negative externalities on the environment. The rise in shipping volumes from fashion e-commerce and returns has led to a rapid increase in environmental impact. However, innovative last-mile delivery solutions such as electric vehicles, cargo bikes, and drones have the potential to reduce environmental impact and improve efficiency. Implementing sustainable modes of delivery, such as delivering on foot or by bicycle, can also contribute to reducing costs and improving sustainability in urban areas. Additionally, the use of shared mobility for home delivery services can create economic benefits but may also lead to increased greenhouse gas emissions. Overall, there is a need for further research to explore the economic feasibility and scalability of sustainable last-mile delivery solutions.

What are the advantages and disadvantages of algorithms for last mile delivery?5 answersAlgorithms for last-mile delivery offer several advantages and disadvantages. One advantage is that they can optimize delivery routes, leading to reduced travel distance and cost. For example, the algorithm proposed by Zeng et al. achieves shorter makespan and lower total latency compared to other methods. Another advantage is the use of machine learning and artificial intelligence techniques, which can improve delivery accuracy and efficiency. Additionally, algorithms can help in predicting accurate time windows for deliveries based on real-time data, leading to better utilization of vehicles and fewer failures. However, there are also some disadvantages. One disadvantage is the complexity of the indoor environment, which poses challenges for existing localization algorithms. Another disadvantage is the need for infrastructure and battery resources to run the algorithms, which can increase costs. Overall, algorithms for last-mile delivery offer significant benefits but also face certain limitations.

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