<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Transportation Workshop: Streets of the Future</title>
      <link>https://rip.trb.org/View/2677682</link>
      <description><![CDATA[With connected and autonomous vehicles (C/AVs), drones, and delivery robots moving from research labs to urban streets, it will not be long before these technologies wind up on city streets. Unfortunately, public and private transportation stakeholders are generally not suited to keep up with technological change, especially when multiplier effects from various strands of innovation can disrupt urban life. Therefore, there is an urgent need to train a future-focused workforce that can adapt from today's best practices and standards, be creative and critical, and come up with innovative options for road safety in the future. Likewise, there is an urgent need to demonstrate to public/private sector stakeholders the most likely transportation changes and challenges over the next two decades. This project has two aims: (i) offer a new graduate-level course focused on using urban corridors as test beds to imagine reasonably accurate future scenarios that are based on state-of-the-art knowledge from the current times, and (ii) to assemble an exhibition where the lessons learned will be shared more broadly with the transportation community through a virtual environment (VR) and posters.]]></description>
      <pubDate>Wed, 04 Mar 2026 16:43:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2677682</guid>
    </item>
    <item>
      <title>Innovations Deserving Exploratory Analysis--The Transit IDEA Program. IDEA 108. TrainMate - Let's Make Public Transportation Public</title>
      <link>https://rip.trb.org/View/2572327</link>
      <description><![CDATA[This is a follow-on project to a recently completed Transit IDEA Project T-100 in which the research team  developed the designs and studied the feasibility of a robotic system, TrainMate, to assist passengers with disabilities at non-accessible street level train transit stations. The project successfully produced electromechanical blueprints as well as software components of the proposed robotic system and ran them through end-to-end software simulations to ensure that they could work in real-life scenarios and be used to take the research to  the prototyping phase  In this Type 2 project, a prototype version of the TrainMate will be built and its capabilities demonstrated in enabling individuals using mobility devices to independently board and deboard trains efficiently and conveniently. 

The project work will involve building a physical full-scale prototype unit with focus on meeting the specific requirements of the users and the transit agencies. In the earlier proof-of-concept project, 3-D models of the system were designed, and the integrated system components were tested in various simulation environments. The results validated the proposed prototype design. Several software systems were  surveyed or developed dealing with artificial intelligence, autonomous navigation, machine vision, robotic operating system, and the speech to text and text to speech capability to establish the feasibility of the integrating software components with the electromechanical components.  In this follow-on project, those software systems will be further developed and tested on the prototype platform to ensure their applicability and useability. The fully working prototype unit (robotic base and the wheelchair lift module) including the sensor network and software components, shall pass all technical verifications and tests conducted in laboratory set-up in a mockup train station. The system will be made ready for pilot program testing. New Jersey Transit will allow access to one of its railyards to test the system in a safe and controlled environment using real railcars before moving on to the public train stations. The TrainMate system will be taken to different train stations identified by the NJ Transit and tested over several months in actual public transportation environments to assess its field readiness, useability, and applicability to serve the intended use. A passengers’ survey will be conducted for their feedback on their satisfaction with the TrainMate system. Calibration and enhancement of the system will continue. Finally, the project will be concluded with a field demonstration before invited officials and NJ Transit executives  at the New Jersey’s Hoboken train station.

The benefits of the robotic TrainMate system are significant, particularly for disabled passengers who require accessible transportation. With TrainMate, this disadvantaged section of the public will no longer have to rely on assistance from others or deal with limited mobility when using public transportation. It will be a safe, reliable, and convenient way for them to travel with confidence, providing them with a greater sense of independence and autonomy. ]]></description>
      <pubDate>Tue, 08 Jul 2025 16:41:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572327</guid>
    </item>
    <item>
      <title>IFC-based BIM for Robotic Installation of Precast Bridge Components</title>
      <link>https://rip.trb.org/View/2491002</link>
      <description><![CDATA[This exploratory project proposes to take an invariant signatures and logic-based artificial intelligence (AI) approach to analyze precast bridge components’ designs in IFC-based BIM for automation in onsite construction using robots. The invariant signature of an AEC object is defined as “a set of intrinsic properties of the object that distinguish it from others and that do not change with data schema, software implementation, modeling decisions, and/or language/cultural contexts”. It has been successfully demonstrated in the vertical construction sector in supporting BIM interoperability: (1) between architectural design and construction cost analysis; (2) between architectural design and code compliance checking; (3) between architectural design and structural analysis; and (4) between architectural design and energy modeling/simulation. It also has been initially tested in the horizontal construction (i.e., civil infrastructure) sector for BIM interoperability between drainage/pavement design and quality assurance analysis. In this project, the invariant signatures will be investigated in applications that support BIM interoperability among precast bridge design, robotic installation, and constructability analysis.]]></description>
      <pubDate>Wed, 22 Jan 2025 12:01:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2491002</guid>
    </item>
    <item>
      <title>AI-based Lift Path Planning for Robotic Installation of Precast Bridge Components</title>
      <link>https://rip.trb.org/View/2491027</link>
      <description><![CDATA[The objective of this exploratory project is to develop and test an AI-based framework for 3D crane lift path planning in robotic installation of precast bridge components via physics-based dynamic simulation. The proposed method will consider the kinematic constraints of crane operation and is adaptive to diverse site configurations and structural designs. If successful, the project will prove the feasibility and benefits of using robotic technologies for installation of precast components in transportation infrastructure. The developed framework will establish the technical foundation and could be potentially extended and implemented in real construction projects with complex design and site conditions, thus improving construction productivity and reducing human efforts.]]></description>
      <pubDate>Tue, 14 Jan 2025 16:12:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2491027</guid>
    </item>
    <item>
      <title>Design and Operational Assessment of a Mobile Robot for Undercarriage Inspection of Railcars</title>
      <link>https://rip.trb.org/View/2446877</link>
      <description><![CDATA[This research assesses the design and track operation of a track crawler robot (TCR) for practical and easy inspection of stationary railcars’ undercarriages in an effort to detect any pending failures or assess any security risk of out-of-sight objects. The research leverages against a robot available at the Railway Technologies Laboratory (RTL) of Virginia Tech and offers improvements to the structure, drive system, imaging devices, and operator remote control to improve the speed, track maneuverability, and duty cycle of the robot.

The TCR includes a drive system consisting of two AC motors that operate a track (like tank tracks). It further includes two GoPro® cameras, light system, and onboard power for approximately one hour of maximum power operation. The details of the TCR design are introduced through its operational requirements, which guided its initial design. The specific design configurations are used to derive the applicable parameters essential for track operation of the robot. The TCR’s subsystems are evaluated individually to assess their strengths and weaknesses, which are then used to guide the specific tasks in improving the overall system’s performance.  The details of the required modifications are included for the imaging, lighting, control, frame structure, and mobility subsystems.  

For each subsystem, test results are used to engineer workable solutions for overcoming the shortcomings or implementing additional functionality. The redesigned system is further evaluated through testing to assess the improvements due to modifications.  Beyond laboratory tests, a final assessment of the system was done on a branch line and mainline track, both with great success.  

The recorded images and operational evaluation of the TCR prove it to be a valuable inspection tool for the railroads to inspect out-of-sight undercarriage components of stationary trains in a railyard or siding, to identify any failed or nearly-failed equipment before they develop into a major or out-of-compliance issue. The TCR also promises to be useful for security agencies to easily and efficiently inspect trains entering secured areas to uncover any suspicious devices.
]]></description>
      <pubDate>Tue, 29 Oct 2024 15:28:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2446877</guid>
    </item>
    <item>
      <title>Certification of Connected and Automated Vehicles for Vulnerable Road Users</title>
      <link>https://rip.trb.org/View/2440019</link>
      <description><![CDATA[The autonomous systems industry in the Pittsburgh region supports 14,900 jobs and $ 1.2 billion in total labor income. It is estimated that within five years, the industry’s total scale will reach $ 10 billion. The key powerhouse is the development of connected and autonomous vehicles (CAVs). Albeit this huge opportunity, one hurdle to this transformative change is the concern of safety. Between 2013 and 2020, 31 states and the District of Columbia enacted legislation related to autonomous vehicles. The impact of state action is starting to manifest through the attraction of efforts that test autonomous systems to regions across the country as companies continue to advance their platforms. Numerous advancements have been developed to mitigate safety risks. For example, simulation tools, closed test grounds, and open corridors have been deployed by companies and universities. 

A critical research topic in safety is the evaluation of safety for vulnerable road users (VRUs), such as wheelchair users, people with strollers, vision-impaired people, service-dog users, and e-scooter users. Failure to ensure those people’s safety may result in criticism and backlash from the public and also objection and pushback from regulators. The primary goal of this project is to address this gap by designing and implementing a systematic CAV evaluation certificate program, along with simulation and physical tools, for VRUs.

This objective presents two challenges: (1) the limited data availability; (2) the lack of mature hardware for testing. The research team plans to address these by leveraging two strengths. 
The first strength is their expertise in multi-fidelity Generative Artificial Intelligence (AI). To provide stringent assessment, the team will leverage their previous work on adversarial, knowledge-based, and data-driven scenario generation to create extensive critical scenarios that pose significant risks to VRUs. PI Zhao has experience in utilizing large language models (LLMs) in autonomous vehicle (AV) legal behavior monitoring. To ensure the coverage of scenarios required by regulations and policies, the team will use similar approaches to assist the scenario design. The team will also utilize their previous work in accelerated evaluation to boost efficiency. These approaches are intended to mitigate the first challenge. 

The second strength is the expertise in both the automotive and robots. The team possesses the expertise to design systems with both autonomous vehicles and VRUs operated by robots. The team will develop a platform that can carry balloon pedestrians/wheelchair users in different terrains and mimic e-scooter users with their wheeled and legged robots. This will offer the advantage of agility and efficiency for self-reconstruction in the event of collisions. Testing robots developed in this project could serve as initial products for a spin-off start-up. 

In the next five years, Pittsburgh will encounter increasing competition from regions with signature state and regional initiatives that support autonomy applications. To maintain its position, Pittsburgh must establish programs to reinforce its current innovation ecosystem and root emerging companies and talent in the region. The team believes this project will establish a unique strength in the CAV safety evaluation area and secure Pittsburgh’s leading role in the field of autonomy. ]]></description>
      <pubDate>Sun, 13 Oct 2024 08:48:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440019</guid>
    </item>
    <item>
      <title>Assessing and Monitoring Performance of Small Culverts</title>
      <link>https://rip.trb.org/View/2437972</link>
      <description><![CDATA[The Vermont Agency of Transportation (VTrans) has adopted a policy requiring all culverts to be inspected every 5 years, resulting in around 9,600 small culverts needing to be inspected annually. Many of these culverts have small diameters that preclude human inspection. Culvert failures can be expensive, such as the recent high-profile collapse under I-89S near Richmond, Vermont (this was a 96” culvert not anticipated in the scope of this project). With improved methods of monitoring and assessing the conditions of culverts, proper maintenance or replacement projects can be planned and implemented before catastrophic failure of a culvert occurs. This project will build on previous research by the research team on improved low-cost robotic culvert inspection systems, i.e., the HIVE 2.0. The research will fine tune the design for low-cost assembly and durability, then build a small fleet of tank-style robots, examine performance, add techniques for enhanced telemetry and surveillance. Additional research includes the development of low-cost flow sensors and explore the use of legged dog robots for culvert inspection.]]></description>
      <pubDate>Wed, 09 Oct 2024 12:07:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2437972</guid>
    </item>
    <item>
      <title>Field Testing Validation of a Robotic Crack Sealer Versus ODOT Traditional Crack Sealing Methods
</title>
      <link>https://rip.trb.org/View/2419760</link>
      <description><![CDATA[As roads deteriorate, transverse and longitudinal cracks open. A common maintenance technique to fill these cracks is to seal them in with an approved material. Crack sealing can often be a challenging task since it is weather dependent, slow-paced, and takes at least seven employees to perform. There are also consistent issues with equipment breakdowns on crack sealing projects. Crack sealing is also a dangerous process as it puts workers directly on the roadway working with high temperature liquid asphalt. These challenges can lead to delayed response times in crack sealing which negatively impacts the pavement and can lead to injuries to the workforce and motoring public. Recognizing these challenges, the research team is pursuing best practices and alternatives that would improve efficiency and response time to perform crack sealing.       

The goal of this research is to enhance Ohio Department of Transportation's (ODOT's) crack sealing processes by validating projected efficiencies in performance and potential cost savings utilizing robotic crack sealing systems.                      ]]></description>
      <pubDate>Tue, 20 Aug 2024 10:36:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2419760</guid>
    </item>
    <item>
      <title>Transforming Infrastructure Inspection by Integrating a UAS with a Continuum Robotic Arm for Potential Contact-Based Damage Assessment</title>
      <link>https://rip.trb.org/View/2412947</link>
      <description><![CDATA[Uncrewed Aerial Systems (UAS) hold promise for revolutionizing the inspection of transportation infrastructure by enabling rapid and safe assessments. However, the application of UAS is predominantly limited to detecting surface-level defects, such as visible cracks, due to the reliance on vision sensors. This approach inherently misses subsurface damage, which, to date, requires direct contact-based methods (e.g., ultrasonic, magnetic, and radiographic techniques) that are currently carried out by manual inspection. This project aims to preliminarily investigate a transformative approach to infrastructure inspection by developing an integrated UAS platform equipped with a continuum robotic arm for contact-based inspection. The project will also conduct a preliminary evaluation of sensors suitable for contact-based infrastructure inspection, providing a basis for future sensor integration efforts. This proposed system aims to establish a foundational approach for future developments in multimodal and autonomous infrastructure inspection, advancing the field by overcoming current limitations in damage assessment capabilities.]]></description>
      <pubDate>Mon, 05 Aug 2024 19:03:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2412947</guid>
    </item>
    <item>
      <title>Development of Automated Non-Destructive Testing Equipment Incorporating Magnetic Based Technology and Ultrasound Technology Combined with Robotic and Artificial Intelligence for Accelerated Inspection of External Tendons</title>
      <link>https://rip.trb.org/View/2382064</link>
      <description><![CDATA[The main objective of the research is to develop similar technologies for accelerated inspection of external tendons, incorporating several new technologies and inspection methods in an inspection equipment. Specifically, project objectives will include, incorporation of a) MFL technology for identifying section loss and areas with corrosion activities, b) Customized robots for accelerated testing, c) Artificial intelligence for assisting operator to make optimized decision, using algorithms that will be developed based on data to be developed under task 3.]]></description>
      <pubDate>Mon, 03 Jun 2024 14:25:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2382064</guid>
    </item>
    <item>
      <title>Deploying Autonomous Robot Delivery System to Replace Truck Delivery and Reduce GHG Emission in Austin, TX</title>
      <link>https://rip.trb.org/View/2359160</link>
      <description><![CDATA[Being fully electric-powered, Autonomous Delivery Robots (ADR) present a promising avenue for substantial reductions in energy utilization and greenhouse gas (GHG) emissions within urban areas. However, existing robot delivery systems have been predominantly tested on small scales such as university campuses, and for specific delivery purposes. The potential environmental benefits of these systems remain largely uncharted, necessitating further exploration and validation. To fill this gap, this research aims to deploy a robot delivery system in an Austin neighborhood to test the performance of ADR in terms of delivery efficiency and GHG emissions reduction, then a citywide robot delivery system deployment strategy will be developed. This study employs a Short to Medium Range Autonomous Delivery System (SMADS) to deliver packages in Georgian Acres Community in Austin, TX. The SMADS which was funded by the UT Good Systems Grand Challenge, is designed to deliver food in the UT campus using robots that will cross complex terrain, navigating around people, cars, and other obstacles typical to campus roadways. The study has two objectives. The first objective aims to deploy a robot delivery system in a real neighborhood to satisfy last-mile delivery demands in residents’ daily lives. PIs will also test and analyze the efficiency and GHG emission reduction of this system. The second objective is to Formulate a comprehensive robotic delivery system deployment strategy for the city of Austin, Texas, drawing upon the findings and analyses from the first objective. The result of this study will instruct autonomous robot delivery system designation and provide local governments with deployment strategies to expand environmental benefits. This research in the Austin case will serve as a focal point for generating insights and solutions pertinent to analogous challenges faced by autonomous robot delivery systems across the U.S.

]]></description>
      <pubDate>Mon, 25 Mar 2024 19:15:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2359160</guid>
    </item>
    <item>
      <title>Gaze-directed UAV-UGV Coordination Framework for Onsite Quality Inspection of Precast Bridge Construction</title>
      <link>https://rip.trb.org/View/2314010</link>
      <description><![CDATA[Precast bridge components such as girders, decks, and columns facilitate accelerated bridge construction while offering improved construction quality due to the high quality control standards at the offsite precast plants. In the offsite precast plants, components need to go through rigorous dimensional and surface quality inspection, after which they are transported to the jobsite for final assembly. Contrastively, onsite quality inspection, which still largely relies on manual visual inspection on limited samples, is yet to match up with the standards of the offsite practices. Onsite quality inspection for precast bridge construction is crucial due to potential defects after the offsite construction phase. For example, damage and defects may occur during the component transportation process. The quality of onsite construction activities such as connection joint sealing, post-poured wet joints, and component localization and alignment, also significantly affect the overall structural integrity and durability. Recently, many sensing systems, such as laser scanning and vision-based systems have been developed for quality inspection of precast components. Most efforts have been dedicated to creating new data processing and analysis algorithms to improve accuracy, with very limited focus on improving the efficiency and accuracy of the data collection process using automated technology. There is a critical need to develop a robot-assisted platform to improve the efficiency and coverage of data collection and inspection for quality assurance/quality control (QA/QC) of onsite precast bridge construction. The objective of this project is to develop a novel gaze-directed unmanned aerial vehicle (UAV)-unmanned ground vehicle (UGV) coordination framework for onsite quality inspection of precast bridge construction. Specifically, UAV will provide global coverage for inspectors to quickly identify the components and construction activities for inspection while UGV will navigate to specific locations for close inspection following human guidance. A new gaze-directed human-machine interface will be developed, where inspectors can express their guidance via natural gaze movements, to reduce worker mental load. By establishing a new multi-robot-human coordination framework with natural and intuitive interactions, this project will develop an efficient and automated infrastructure inspection approach, thus improving the quality and durability and eventually extending the life of precast transportation infrastructure.]]></description>
      <pubDate>Sun, 24 Dec 2023 08:41:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2314010</guid>
    </item>
    <item>
      <title>Exploring the Use of Ground-Based Robotic Assistance in Uncrewed Operations of State DOTs
</title>
      <link>https://rip.trb.org/View/2307269</link>
      <description><![CDATA[The Ohio Uncrewed Aircraft Systems (UAS) Center's new initiatives are leading the nation with research and development to improve airspace and utilize the many possibilities of uncrewed aircraft operation. Based on direction from the Federal Aviation Administration (FAA), the current state of practice requires humans maintaining a visual line of sight during operation. This is referred to as a Visual Observer (VO).  While human analysis can be imperative in ensuring safety for all aspects of the uncrewed aircraft, there are limitations (e.g., visual field, distraction) that can negatively impact these efforts. The threat of ground-based obstacles (e.g., trees, birds, topography) and air traffic (e.g., crewed airplanes, other drones) are also challenging, especially when there is more than one threat present during an operation. It is not uncommon to deploy teams of two or more people for uncrewed operations to assist with gear, site set-up, and provide multiple VOs. This practice consumes the limited resources available, which can negatively impact the overall efficiency of this specialized workforce. With the assistance of a ground based robotic assistant, a variety of advanced sensors and equipment could be carried to the destination and collection of ground-based data to augment the airborne data that is being collected would also be a possibility. Robotic assistants could be sent to different locations and terrain, some hazardous for humans to access as well. The goal of this research is to determine the viability of ground-based robotic assistants for UAS and Ohio Department of Transportation (ODOT) operations. 
The objectives for this project are: (1) Develop concepts for operational use of ground-based robotic assistants for ODOT business functions to support uncrewed aircraft operations. (2) Evaluate existing technologies and tools utilized/developed by ODOT offices/districts that could be used to support (or be supported by) robotic assistants (e.g., pavement markings for autonomous vehicles, shoulder drop-off readings, pothole 
  detection, culvert inspections). (3) Identify existing technologies and tools, readily available on the market, that could be utilized with or augmented by ODOT's existing technologies/accessories to support the identified concepts. (4) Develop an implementation guide for the various recommendations based on best use-case scenarios for each recommendation. 
]]></description>
      <pubDate>Mon, 11 Dec 2023 15:15:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2307269</guid>
    </item>
    <item>
      <title>AI-enabled ILI robot with integrated structured light NDE for distribution pipelines</title>
      <link>https://rip.trb.org/View/2085754</link>
      <description><![CDATA[The project will develop and demonstrate a field test-ready structured light tool integrated with a highly flexible snake robot, which can adapt to the complex environment inside distribution pipelines.]]></description>
      <pubDate>Fri, 16 Dec 2022 14:15:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2085754</guid>
    </item>
    <item>
      <title>Towards Privacy-Preserving Networked Autonomous Mobility: Analysis, Tools Development, and Real-World Evaluation</title>
      <link>https://rip.trb.org/View/1981124</link>
      <description><![CDATA[The research team aims to investigate how networked autonomous mobility, such as self-driving taxis or delivery robots, will reshape our understanding of privacy and explore technical tools for privacy-preserving operation on the individual level and group level. The team will conduct comprehensive and realistic analysis using public datasets collected by self-driving companies and verify the feasibility to deploy our algorithms with edge computing on delivery robots developed by our lab. The team will report their findings to their industrial partners Bosch and Uber and Attorney General and ACLU of Penn.]]></description>
      <pubDate>Fri, 10 Jun 2022 14:38:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1981124</guid>
    </item>
  </channel>
</rss>