Negative obstacle detection using LiDAr sensors for a robotic wheelchair

Taylor E. Baum, Joseph P. Chobot, Kelilah L. Wolkowicz, Sean N. Brennan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


The objective of this work is to develop a negative obstacle detection algorithm for a robotic wheelchair. Negative obstacles – depressions in the surrounding terrain including descending stairwells, and curb drop-offs – present highly dangerous navigation scenarios because they exhibit wide characteristic variability, are perceptible only at close distances, and are difficult to detect at normal operating speeds. Negative obstacle detection on robotic wheelchairs could greatly increase the safety of the devices. The approach presented in this paper uses measurements from a single-scan laser range-finder and a microprocessor to detect negative obstacles. A real-time algorithm was developed that monitors time-varying changes in the measured distances and functions through the assumption that sharp increases in this monitored value represented a detected negative obstacle. It was found that LiDAR sensors with slight beam divergence and significant error produced impressive obstacle detection accuracy, detecting controlled examples of negative obstacles with 88% accuracy for 6 cm obstacles and above on a robotic development platform and 90% accuracy for 7.5 cm obstacles and above on a robotic wheelchair. The implementation of this algorithm could prevent life-changing injuries to robotic wheelchair users caused by negative obstacles.

Original languageEnglish (US)
Title of host publicationModeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrations and Control of Systems; Vibrations
Subtitle of host publicationModeling, Analysis, and Control
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791851913
StatePublished - Jan 1 2018
EventASME 2018 Dynamic Systems and Control Conference, DSCC 2018 - Atlanta, United States
Duration: Sep 30 2018Oct 3 2018

Publication series

NameASME 2018 Dynamic Systems and Control Conference, DSCC 2018


OtherASME 2018 Dynamic Systems and Control Conference, DSCC 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


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