Autonomous Delivery System Research Articles

Zara Tech Trail: Futuristic Autonomous Robocart for Cutting-Edge Multi-Perspective Delivery System

The autonomous delivery system presented in this project utilizes a RoboCart equipped with GPS navigation to seamlessly transport products from source to destination. The system, powered by electric charging, ensures timely and secure delivery to end customers, featuring a specialized hand gripper for careful product handling. Designed for diverse applications such as commercial purposes, personal use, and industries including hotels, this fully autonomous cart incorporates a password enabled security feature to guarantee user verification. Positioned as a cutting-edge technological solution, this project aims to effectively address the last mile delivery challenge, presenting a potential to significantly reduce delivery times and contribute to societal benefits. The success of this initiative holds the promise of substantial positive impacts on the Society.

Sharing instant delivery UAVs for crowdsensing: A data-driven performance study

In recent years, there has been a significant increase in demand for instant deliveries, such as rapid delivery of takeaway food and medicine. Many logistics companies are planning to realize real-time delivery services through unmanned aerial vehicles (UAVs). However, costs of running such an autonomous delivery system are too expensive. Fortunately, urban management departments (UMD) that are responsible for monitoring urban environments have the intentions to cooperate with external companies and design crowdsensing methods, so that the duty can be ensured in an autonomous and low-cost method. Motivated by this, instant delivery UAVs are introduced into crowdsensing, so that UAVs can earn additional money for logistics companies while ensuring priority completion of instant deliveries, as well as provide environmental monitoring data to UMD. To be specific, some challenges are firstly identified to combine these two tasks based on real-world data in China. Then a set of data-driven UAVs sharing algorithms is proposed to achieve sensing for urban POIs (Points of Interest) and urban anomaly events. The results of simulations based on real-world datasets demonstrate the efficiency of our methods. The average sensing interval of the POIs within a 368.64π km2 area can be only ≈2.6 min. more than 81% of stochastic events are successfully predicted, over 84% (401 out of 476) of events are successfully sensed, and 60% of events have a UAV to arrive within 6.65min of their occurrence.

DecentraliDrone: A decentralized, fully autonomous drone delivery system for reliable, efficient transport of goods

As the world becomes more interconnected and globalized, the demand for efficient and reliable delivery systems is on the rise. To meet this demand, we propose DecentraliDrone, an innovative drone delivery system that utilizes advanced technology to transport goods autonomously and efficiently. Our system is fully decentralized, meaning that it operates without the need for human intervention and relies on a distributed network of drones and devices to function. One of the key features of DecentraliDrone is its autonomy. The system operates without the need for human intervention, enabling it to operate 24/7 and reducing the risk of accidents caused by human error. Additionally, our system is highly scalable, allowing it to adapt to changing demands and expand its delivery capabilities. There are several challenges that must be addressed in order to fully realize the potential of DecentraliDrone. These include developing robust communication protocols, ensuring the safety and security of the system, and addressing regulatory and legal issues related to autonomous delivery systems.

Supply chain resilience: how autonomous rovers empirically provide relief to constrained flight line maintenance activities

PurposeThe purpose of this research is to explore the utility of autonomous transport across two independent airframe maintenance operations at a single location.Design/methodology/approachThis study leveraged discrete event simulation that encompassed real-world conditions on a United States Air Force flight line. Though the Theory of Constraints (TOC) lens, a high-demand, human-controlled delivery asset is analyzed and the impact of introducing an autonomous rover delivery vehicle is assessed. The authors’ simulations explored varying numbers and networks of rovers as alternative sources of delivery and evaluated these resources’ impact against current flight line operations.FindingsThis research indicates that the addition of five autonomous rovers can significantly reduce daily expediter delivery tasks, which results in additional expertise necessary to manage and execute flight line operations. The authors assert that this relief would translate into enhancements in aircraft mission capable rates, which could increase overall transport capacity and cascade into faster cargo delivery times, systemwide. By extension, the authors suggest overall inventory management could be improved through reduction in transportation shipping time variance, which enhances the Department of Defense’s overall supply chain resilience posture.Originality/valueWhen compared against existing practices, this novel research provides insight into actual flight line movement and the potential benefits of an alternative autonomous delivery system. Additionally, the research measures the potential savings in the workforce and vehicle use that exceeds the cost of the rovers and their employment.

Robust and Repeatable Biofabrication of Bacteria‐Mediated Drug Delivery Systems: Effect of Conjugation Chemistry, Assembly Process Parameters, and Nanoparticle Size

Bacteria‐mediated drug delivery systems comprising nanotherapeutics conjugated onto bacteria synergistically augment the efficacy of both therapeutic modalities in cancer therapy. Nanocarriers preserve therapeutics’ bioavailability and reduce systemic toxicity, while bacteria selectively colonize the cancerous tissue, impart intrinsic and immune‐mediated antitumor effects, and propel nanotherapeutics interstitially. The optimal bacteria–nanoparticle (NP) conjugates will carry the maximal NP load with minimal motility speed hindrance for effective interstitial distribution. Furthermore, a well‐defined and repeatable NP attachment density distribution is crucial to determining these biohybrid systems’ efficacious dosage and robust performance. Herein, our nanoscale bacteria‐enabled autonomous delivery system (NanoBEADS) platform is utilized to investigate the effects of assembly process parameters of mixing method, volume, and duration on NP attachment density and repeatability. The effect of linkage chemistry and NP size on NP attachment density, viability, growth rate, and motility of NanoBEADS is also evaluated. It is shown that the linkage chemistry impacts NP attachment density while the self‐assembly process parameters affect the repeatability and, to a lesser extent, attachment density. Lastly, the attachment density affects NanoBEADS’ growth rate and motility in an NP size‐dependent manner. These findings will contribute to the development of scalable and repeatable bacteria–NP biohybrids for applications in drug delivery and beyond. An interactive preprint version of the article can be found here: https://www.authorea.com/doi/full/10.22541/au.163100509.93917936.

Autonomous ballistic airdrop of objects from a small fixed-wing unmanned aerial vehicle

Autonomous airdrop is a useful basic operation for a fixed-wing unmanned aerial system. Being able to deliver an object to a known target position extends operational range without risking human lives, but is still limited to known delivery locations. If the fixed-wing unmanned aerial vehicle delivering the object could also recognize its target, the system would take one step further in the direction of autonomy. This paper presents a closed-loop autonomous delivery system that uses machine vision to identify a target marked with a distinct colour, calculates the geographical coordinates of the target location and plans a path to a release point, where it delivers the object. Experimental results present a visual target estimator with a mean error distance of 3.4 m and objects delivered with a mean error distance of 5.5 m.

Modeling, prediction, and anomaly detection of manned-vehicle behavior in open field based on velocity vector and variance tensor fields

ABSTRACT The introduction of autonomous delivery systems is expected to improve productivity in numerous application fields. Since such systems comprise both unmanned and manned units, it is important to control the whole system using appropriate behavioral models of manned vehicles or humans. This work builds on the results of a previous study, where a velocity vector field was used to model human movement, and extends the use of velocity vector fields to the simulation of vehicle behavior by modeling variance in given trajectories. This extension enables the velocity vector field to regenerate an average trajectory with a band and consequently allows one to calculate the vehicle's predicted region in arbitrary future time steps and anomaly detection. These applications are expected to facilitate collision avoidance and should help one to control other applications of unmanned vehicles.

On the Image Sensor Processing for Lane Detection and Control in Vehicle Lane Keeping Systems.

Lane keeping systems for a keeping a vehicle in the desired lane is key to advanced driving assistance system in autonomous vehicles. This paper presents a cost-effective image sensor with efficient processing algorithm for lane detection and lane control applications to autonomous delivery systems. The algorithm includes (1) lane detection by inverse perspective mapping and random sample consensus parabola fitting and (2) lane control by pure pursuit steering controller and classical proportional integral speed controller based on a nonholonomic kinematic model. The image sensor experiments conducted on a 1/10 scale model car maneuvering in a straight–curve–straight lane validate the better processing performance before, during, and after the turning section over previous work. The image sensor with the processing algorithm achieves the average lane detection error within 5% and maximum cross-track error within 9% in real-time. The development shall pave the way to cost-effective autonomous delivery systems.

Hybrid centralized/decentralized control of a network of bacteria-based bio-hybrid microrobots

Engineering microrobotic systems using a bio-hybrid approach that couples synthetic micro/nanoscale components with live cells is a powerful approach to address key shortcomings of micro/nanotechnology such as providing an on-board power source, communication, and decentralized control. In the last decade, a number of centralized control strategies based on native biological mechanisms have been demonstrated; however, decentralized cooperative control of a swarm of bio-hybrid microrobots has rarely been explored. In this work, we use our previously developed agent-based computational model to explore the utility of integrating synthetic biology with bio-hybrid microrobotics with the goal of developing strategies for robust decentralized control of bio-hybrid microrobotic systems. To this end, our Nanoscale Bacteria-Enabled Autonomous Delivery System (NanoBEADS), wherein each agent is comprised of an Escherichia coli bacterium conjugated with an ensemble of nanoparticles, was used as a model system. We imparted bacteria with engineered biological circuits to facilitate agent-agent communication and enable predictable and robust cooperative control of a network of NanoBEADS agents. We developed a hybrid control strategy wherein a centralized chemotaxis-based control scheme is used to direct migration, and a decentralized quorum sensing (QS)-based control scheme enables the agents to independently coordinate a cooperative behavior (fluorescent protein expression). We then computationally analyzed the role of bacterial subsystem properties and spatiotemporal changes in population organization in the emergent behavior (i.e., the activation time of the QS response) of the NanoBEADS. We show that the spatial changes in NanoBEADS agents’ distribution, in response to the centralized control input, and the mass transport boundary conditions are critical factors in determining the dynamics as well as the sensitivity and robustness of the QS response. We analyze the stark differences in feasible QS parameter domains under dissimilar chemoattractant gradients and show that the flow boundary conditions may greatly influence system function. Altogether, we show that a bacteria-based bio-hybrid system must be designed with careful consideration of growth rate, chemotaxis properties, QS properties, and the environment initial and boundary conditions in the context of its target application. This modeling framework can serve as an insightful tool for the predictive design of bio-hybrid microrobotic swarms with a tunable and robust response.

Development of Cloud-Based UAV Monitoring and Management System.

Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to revolutionize commercial industries and the public domain outside of the military. UAVs would be able to speed up rescue and recovery operations from natural disasters and can be used for autonomous delivery systems (e.g., Amazon Prime Air). An increase in the number of active UAV systems in dense urban areas is attributed to an influx of UAV hobbyists and commercial multi-UAV systems. As airspace for UAV flight becomes more limited, it is important to monitor and manage many UAV systems using modern collision avoidance techniques. In this paper, we propose a cloud-based web application that provides real-time flight monitoring and management for UAVs. For each connected UAV, detailed UAV sensor readings from the accelerometer, GPS sensor, ultrasonic sensor and visual position cameras are provided along with status reports from the smaller internal components of UAVs (i.e., motor and battery). The dynamic map overlay visualizes active flight paths and current UAV locations, allowing the user to monitor all aircrafts easily. Our system detects and prevents potential collisions by automatically adjusting UAV flight paths and then alerting users to the change. We develop our proposed system and demonstrate its feasibility and performances through simulation.

Biomanufacturing and self-propulsion dynamics of nanoscale bacteria-enabled autonomous delivery systems

Flagellated bacteria have superb self-propulsion capabilities and are able to effectively move through highly viscous fluid and semi-solid (porous) environments. This innate aptitude has been harvested for whole-cell actuation of bio-hybrid microrobotic systems with applications in directed transport and microassembly. In this work, we present the biomanufacturing of Nanoscale Bacteria-Enabled Autonomous Delivery Systems (NanoBEADS) by controlled self-assembly and investigate the role of nanoparticle load on the dynamics of their self-propulsion in aqueous environments. Each NanoBEADS agent is comprised of spherical polystyrene nanoparticles assembled onto the body of a flagellated Escherichia coli bacterium. We demonstrate that the NanoBEADS assembly configuration is strongly dependent upon the nanoparticles to bacteria ratio. Furthermore, we characterized the stochastic motion of the NanoBEADS as a function of the quantity and size of the nanoparticle load and computationally analyzed the effect of the nanoparticle load on the experienced drag force. We report that the average NanoBEADS swimming speed is reduced to 65% of the free-swimming bacteria speed (31 μm/s) at the highest possible load. NanoBEADS can be utilized as single agents or in a collaborative swarm in order to carry out specific tasks in a wide range of applications ranging from drug delivery to whole cell biosensing.