Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. We model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12\%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines.

Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics has been put forward as a high impact candidate for replacing LGVs, with experts estimating over half of urban van deliveries being replaceable by cargo bikes, due to their faster speeds, shorter parking times and more efficient routes across cities. By modelling the relative delivery performance of different vehicle types across urban micro-regions, machine learning can help operators evaluate the business and environmental impact of adding cargo-bikes to their fleets. In this paper, we introduce two datasets, and present initial progress in modelling urban delivery service time (e.g. cruising for parking, unloading, walking). Using Uber's H3 index to divide the cities into hexagonal cells, and aggregating OpenStreetMap tags for each cell, we show that urban context is a critical predictor of delivery performance.

Delivery robots aim to achieve high precision to facilitate complete autonomy. A precise three-dimensional point cloud map of sidewalk surroundings is required to estimate self-location. With or without the loop closing method, the cumulative error increases gradually after mapping for larger urban or city maps due to sensor drift. Therefore, there is a high risk of using the drifted or misaligned map. This article presented a technique for fusing GPS to update the 3D point cloud and eliminate cumulative error. The proposed method shows outstanding results in quantitative comparison and qualitative evaluation with other existing methods.

The logistics of transporting a package from a storage facility to the consumer's front door usually employs highly specialized robots often times splitting sub-tasks up to different systems, e.g., manipulator arms to sort and wheeled vehicles to deliver. More recent endeavors attempt to have a unified approach with legged and humanoid robots. These solutions, however, occupy large amounts of space thus reducing the number of packages that can fit into a delivery vehicle. As a result, these bulky robotic systems often reduce the potential for scalability and task parallelization. In this paper, we introduce LIMMS (Latching Intelligent Modular Mobility System) to address both the manipulation and delivery portion of a typical last-mile delivery while maintaining a minimal spatial footprint. LIMMS is a symmetrically designed, 6 degree of freedom (DoF) appendage-like robot with wheels and latching mechanisms at both ends. By latching onto a surface and anchoring at one end, LIMMS can function as a traditional 6-DoF manipulator arm. On the other hand, multiple LIMMS can latch onto a single box and behave like a legged robotic system where the package is the body. During transit, LIMMS folds up compactly and takes up much less space compared to traditional robotic systems. A large group of LIMMS units can fit inside of a single delivery vehicle, opening the potential for new delivery optimization and hybrid planning methods never done before. In this paper, the feasibility of LIMMS is studied and presented using a hardware prototype as well as simulation results for a range of sub-tasks in a typical last-mile delivery.

The new shipping industry is nearly here. Amazon Inc. just applied for a patent on a new package delivery system capable of shipping consumer goods from the primary delivery vehicle to your door via a mini-sized delivery vehicle robot that ferries shipments to final end-point destinations, according to a filing with the United States Patent and Trademark Office. But we could still get the flying drone deliveries we secretly crave. In the last decade, Amazon and other delivery services have investigated the possibility of employing new technologies to transport packages from warehouses to consumers. The proposals have ranged from driverless vans housing smaller robots to flying drones that ship directly through the air to customer airspace (and then a parachute drop of packages). Electronic delivery robots were assumed to be a cheaper alternative to hiring and supporting humans to perform hard labor.

Tire manufacturers are unveiling new smart tires, complete with intelligent AI software seeking to lend a helping hand to drivers. Tire makers such as Goodyear and Bridgestone have teamed up with AI software developers to create self-detecting tires with the ability to notify drivers when they require a change. The intelligence may help mitigate potential hazards down the line. The first on-the-road testers are last-mile delivery vehicles delivering pertinent data to cloud-computing platforms to provide real-time information using intelligent AI design. The innovation is not ready to be implemented on a mass scale, but the possibilities already are being weighed by experts.

The technology is geared toward vehicles that specialize in last-mile delivery, which refers to the final step in getting packages from a distribution center to the customer. The market for last-mile delivery has picked up as online shopping has soared during the coronavirus pandemic. Goodyear's new technology, announced Wednesday, is called SightLine and includes a sensor and proprietary machine-learning algorithms that can predict flat tires or other issues days ahead of time, by measuring tire wear, pressure, road-surface conditions and many other factors. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. The surge of last-mile deliveries during the pandemic means that a lot of vehicles are on the road, "stopping and going, hitting curbs, causing damage to the tires, causing breakdowns and congestion," said Richard Kramer, chief executive of Akron, Ohio-based Goodyear.

Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace, while taking into account that productivity gains also make society wealthier and thereby contribute to additional labor demand. This analysis enables ethically-minded companies creating or deploying AI systems as well as researchers and policymakers to take into account the effects of their actions on labor markets and inequality, and therefore to steer progress in AI in a direction that advances shared prosperity and an inclusive economic future for all of humanity.

Declaring "it's no longer a question of if...but when" autonomous vehicles are used in retail, President and CEO of Walmart (NYSE:WMT) U.S. John Furner announced the retail titan's intention to invest in General Motors' (NYSE:GM) Cruise self-driving car company in a press release today. Furner said the move will "aid our work toward developing a last mile delivery ecosystem that's fast, low-cost and scalable." The Walmart investment brings the total of Cruise's most recent funding round to $2.75 billion, though neither GM nor Cruise provides specifics on how much each individual company contributes to the whole, CNBC reports. Other investors in the subsidiary include GM itself, Microsoft, Honda Motor, and institutional investors. Among other projects, Cruise intends to roll out self-driving taxis in Dubai within the next two years.

Amazon plans to install AI-enabled cameras in their delivery vehicles to monitor their drivers while they're on the clock. An Amazon video uploaded to Vimeo shows how the camera operates. The Driveri platform, supplied by the software company Netradyne, provides real-time feedback to a driver and evaluates a driver's performance during their shifts. The program's feedback notes distracted driving, failure to stop at a stop sign, speeding and others. If the camera detects any of these trigger signals, it will upload recorded footage.