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The Robotics Institute Carnegie Mellon University

When robots and automation do our most basic work, making it relatively easy for us to be fed, clothed, and sheltered, then we are free to ask, “What are humans for?” Industrialization did more than just extend the average human lifespan. It led a greater percentage of the population to decide that humans were meant to be ballerinas, full-time musicians, mathematicians, athletes, fashion designers, yoga masters, fan-fiction authors, and folks with one-of-a kind titles on their business cards. With the help of our machines, we could take up these roles; but of course, over time, the machines will do these as well. We’ll then be empowered to dream up yet more answers to the question “What should we do?” It will be many generations before a robot can answer that.

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Current GNSS solutions can only provide information for basic applications such as GPS direction assistance. The position estimate to satisfy other requirements can be improved by incorporating information from a map or environment model. A key area of research in ITS is the fusion of available sensor information to provide a high integrity position and state estimate. An additional important constraint is the use of low cost sensors to increase the uptake of this technology into the general vehicle population. This information is essential in modern vehicle safety and automation applications. Contact or

Collision prediction and avoidance under uncertainty - All positioning systems feature uncertainty in the reported position. Any system for predicting and avoiding collisions between intelligent vehicles must account for this uncertainty. Probabilistic models of vehicle conflicts present a promising avenue for tackling this problem. This work can be further extended to examine trustworthiness of state information shared by other vehicles, and develop a method of obtaining maximum value from the information given its trustworthiness. Trustworthiness may be influenced by sensing uncertainty, sensor failure or deliberate falsehood. Detecting and acting on this will be vital for any future intelligent vehicle fleet. Contact or

Urban Street Mapping - This project addresses the evaluation of global maps to assist vehicle navigation. The main goal is the determination of complete set of feature to achieve high integrity navigation with the level of accuracy for autonomous operations.

Road/Lane Maps: The first stage will look at mapping of all vehicle / cycle / ramps lanes. The project will investigate algorithm to be used with high quality perception sensors. It will further extend these techniques to be used with low cost sensors for rapid deployment in all roads around Australia. It will also research into other type of infrastructure that could be sensed to improve the integrity of localisation. The aim of this infrastructure will be reliable detection by multimodal sensing to improve the integrity under all weather conditions. This will also look at the combination of radar signal processing and beacon design to be able to localise under all weather conditions.

3D salient Features: Road infrastructure will play an important role for localisation, specially in motorways, country roads and open areas. Urban areas will present different challenges since high dense traffic could prevent detection of visual artificial landmarks at road level. Nevertheless, the combination of building and infrastructure usually presents very high density salient features that remain static with time. This project will develop methods to obtain navigation maps that can be downloaded in an efficient manner through V2I infrastructure to be used by map matching algorithm to register current mobile position. Contact or

Data Analytics for Naturalistic Driving - Large scale trials of Intelligent Transportation Systems will generate vast amounts of data that is reflective of real world scenarios rather than artificially constrained by experimental design. Methods will need to be developed to capture, warehouse and analyse this information. Vehicles are equipped with sensors which can directly measure the vehicle state. This data allows algorithms to generate hypotheses about the behaviour of the driver and the vehicle. However, measurements of the vehicle state only form indirect observations of driver behaviour. It is difficult to directly measure human actions without biasing their behaviour. Utilising non-invasive measurement technologies such as estimating head orientation, gaze tracking and driver posture provides a means for collecting direct observations of the driver. This expressive data can be used by learning algorithms to model driver behaviour in natural driving conditions. Contact , or

Driver Intent - Human drivers possess a natural ability to perceive our surroundings from the point of view of other people and reason about their intentions. To drive safely, we must develop a high-level understanding of the situation. The estimation of driver intent is a key research area in the domain of Intelligent Transportation Systems. Mathematical tools are required to take a range of vehicle sensor information and attempt to match the reasoning carried out by a human driver. The resulting estimates will form the basis for collision risk assessment and probabilistic decision-making. Contact or

Transport Safety Evaluation and Monitoring - As Intelligent Transportation Systems are developed and deployed there will be a need for evaluating the impact upon safety that they bring. High quality, high frequency data will be available from a variety of sources (in-vehicle sensors, roadside infrastructure) that have not been analysed before. By developing techniques for analysing this data, transport safety can be evaluated and resources targeted at the highest risk areas. This analysis will also be necessary to justify the investments in ITS as they are deployed, and to monitor their effect and ensure that they are achieving their aim of increasing safety. Contact or

Pedestrian / bicycle detection / intent prediction - Autonomous systems can be designed to operate reliably in highly structured environments. Although road-rules provide structure to traffic situations, roads frequently contain unpredictable conditions and scenarios which can violate assumptions. Pedestrians and cyclists are an important example, particularly in densely populated urban environments. To minimise the risk autonomous systems impose on these vulnerable road users, systems must be designed specifically to consider them. The goal is to identify pedestrians, model their trajectories and predict their intent. To achieve these goals, research in this area will draw from the fields of perception, computer vision, filtering and machine learning. Contact or

Urban Perception - For the first time in history, more than half of the world’s population are living in urban centres, so the development of autonomous robotic technology in urban environments represents a significant opportunity. Solving the existing technical barriers to deployment will enable a suite of new applications in diverse areas such as urban disaster response (police, fire, ambulance), surveillance and security, intelligent transportation and autonomous passenger vehicles. The primary challenges are in robust perception (addressed by this topic) and localisation (addressed by “Robust Multi-Modal Localisation and Mapping). You will conduct research in urban perception, addressing the difficulties of modelling a complex, cluttered three dimensional, dynamic environment. Areas of study include recognising components of the scene at different scales (e.g suburb, carpark, car, wheel), tracking complex sequences of motion and interaction and reasoning about change on different temporal scales. Furthermore, research will be done to understand the relationship between sensor information from different sensors and perspective, to enable active robot control for optimal picture compilation. Contact:

Dissertation Or Thesis On Robotics

Participants and sponsors include, in addition to the University, HolmhedSystems AB, Mobile Robotics AB, EU and Volvo Research Foundation.

It may be hard to believe, but before the end of this century, 70 percent of today’s occupations will likewise be replaced by automation. Yes, dear reader, even you will have your job taken away by machines. In other words, robot replacement is just a matter of time. This upheaval is being led by a second wave of automation, one that is centered on artificial cognition, cheap sensors, machine learning, and distributed smarts. This deep automation will touch all jobs, from manual labor to knowledge work.

First, machines will consolidate their gains in already-automated industries. After robots finish replacing assembly line workers, they will replace the workers in warehouses. Speedy bots able to lift 150 pounds all day long will retrieve boxes, sort them, and load them onto trucks. Fruit and vegetable picking will continue to be robotized until no humans pick outside of specialty farms. Pharmacies will feature a single pill-dispensing robot in the back while the pharmacists focus on patient consulting. Next, the more dexterous chores of cleaning in offices and schools will be taken over by late-night robots, starting with easy-to-do floors and windows and eventually getting to toilets. The highway legs of long-haul trucking routes will be driven by robots embedded in truck cabs.

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All the while, robots will continue their migration into white-collar work. We already have artificial intelligence in many of our machines; we just don’t call it that. Witness one piece of software by Narrative Science (profiled in issue 20.05) that can write newspaper stories about sports games directly from the games’ stats or generate a synopsis of a company’s stock performance each day from bits of text around the web. Any job dealing with reams of paperwork will be taken over by bots, including much of medicine. Even those areas of medicine not defined by paperwork, such as surgery, are becoming increasingly robotic. The rote tasks of any information-intensive job can be automated. It doesn’t matter if you are a doctor, lawyer, architect, reporter, or even programmer: The robot takeover will be epic.

We have preconceptions about how an intelligent robot should look and act, and these can blind us to what is already happening around us. To demand that artificial intelligence be humanlike is the same flawed logic as demanding that artificial flying be birdlike, with flapping wings. Robots will think different. To see how far artificial intelligence has penetrated our lives, we need to shed the idea that they will be humanlike.

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Consider Baxter, a revolutionary new workbot from Rethink Robotics. Designed by Rodney Brooks, the former MIT professor who invented the best-selling Roomba vacuum cleaner and its descendants, Baxter is an early example of a new class of industrial robots created to work alongside humans. Baxter does not look impressive. It’s got big strong arms and a flatscreen display like many industrial bots. And Baxter’s hands perform repetitive manual tasks, just as factory robots do. But it’s different in three significant ways.

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Cheung, Visual Hull Construction, Alignment and Refinement for Human Kinematic Modeling, Motion Tracking and Rendering, Technical Report CMU-RI-TR-03-44, PhD Thesis, Robotics Institute, Carnegie Mellon University, October 2003.

Emaro Genoa | - Robotics Program

First, it can look around and indicate where it is looking by shifting the cartoon eyes on its head. It can perceive humans working near it and avoid injuring them. And workers can see whether it sees them. Previous industrial robots couldn’t do this, which means that working robots have to be physically segregated from humans. The typical factory robot is imprisoned within a chain-link fence or caged in a glass case. They are simply too dangerous to be around, because they are oblivious to others. This isolation prevents such robots from working in a small shop, where isolation is not practical. Optimally, workers should be able to get materials to and from the robot or to tweak its controls by hand throughout the workday; isolation makes that difficult. Baxter, however, is aware. Using force-feedback technology to feel if it is colliding with a person or another bot, it is courteous. You can plug it into a wall socket in your garage and easily work right next to it.

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