Proximity Sensors for a High-Bandwidth, Low-Latency Robotic Manipulation Object Avoidance Controller
Name
han-jess7654-sm-meche-2024-thesis.pdf
Description
Thesis PDF
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13.77 MB
Format
Adobe PDF
Checksum (MD5)
b2a8fb103c16b4e1ca1dc44ffdefe578
Author(s)
Han, Jessica
Advisor(s)
Kim, Sangbae
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
Robotics holds the promise of transforming industries, from automating recycling to managing household chores, by enabling machines to perform tasks with human-like dexterity. However, current robotic manipulation systems struggle to achieve the real-time responsiveness required for such tasks. Traditional systems rely on cameras, which slow down control loops with dense and difficult-to-process data. This thesis addresses the need for real-time control in robotic manipulation by utilizing proximity sensors in a high-bandwidth, low-latency object avoidance reflex controller on the Biomimetic Robotics Lab’s dexterous robotic manipulation platform. The research focuses on the two most viable proximity sensors for robotic manipulation: the STMicroelectronics VL6180X Time-of-Flight sensor and the Thinker Phase-Modulated-Light sensor. These sensors are characterized based on their measurement range, error, variance, field-of-view, and convergence time to determine their usability in an object avoidance reflex. Following characterization, a study on the integration of these sensors into the manipulation platform is performed to assess sensing latency and bandwidth implications. Finally, validation of the optimal sensor-controller configuration for the object avoidance reflex—averaging two time-of-flight sensors with a linear virtual force—shows an improvement in bandwidth from 33 Hz to 115 Hz, enhancing the reactivity and stability of the object avoidance reflex. Overall, this research provides a comprehensive study on the individual sensor and sensor-integration levels of proximity sensors for object avoidance reflexes. It enables future researchers to be confident in the manipulation platform’s performance for further controls-level research.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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