Online System Identification and Calibration of Dynamic Models for Autonomous Ground Vehicles

Aghli, Sina; Heckman, Christoffer

doi:10.1109/icra.2018.8460691

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Autonomous vehicles benefit from utilizing high-fidelity dynamical models for planning, control and state estimation. Certain parameters of a dynamical model can only be measured while the car is in certain states, which in many cases can be difficult (Aghli and Heckman, 2018). For instance, knowledge of the static coefficient of friction between the tire and ground ensures the vehicle can turn at high speed without slipping.…”

Section: Smart Tiresmentioning

confidence: 99%

Materials that make robots smart

Hughes

Heckman

Correll

2019

The International Journal of Robotics Research

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We posit that embodied artificial intelligence is not only a computational, but also a materials problem. While the importance of material and structural properties in the control loop are well understood, materials can take an active role during control by tight integration of sensors, actuators, computation, and communication. We envision such materials to abstract functionality, therefore making the construction of intelligent robots more straightforward and robust. For example, robots could be made of bones that measure load, muscles that move, skin that provides the robot with information about the kind and location of tactile sensations ranging from pressure to texture and damage, eyes that extract high-level information, and brain material that provides computation in a scalable manner. Such materials will not resemble any existing engineered materials, but rather the heterogeneous components out of which their natural counterparts are made. We describe the state-of-the-art in so-called “robotic materials,” their opportunities for revolutionizing applications ranging from manipulation to autonomous driving by describing two recent robotic materials, a smart skin and a smart tire in more depth, and conclude with open challenges that the robotics community needs to address in collaboration with allies, such as wireless sensor network researchers and polymer scientists.

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Section: Smart Tiresmentioning

confidence: 99%