Model-based sensor fusion and filtering for localization of a semi-autonomous robotic vehicle

Abstract

This paper refines a physically-inspired model governing the dynamic motion of a vehicle. We present a method used to perform experimental parameter calibration, and then use this model to build an observer (an extended Kalman filter). Experimental results with a robotic vehicle fitted with a prototype kit focus on recovering the truthful real-world information in the context of systematic errors (a faulty wheel encoder sensor), randomly occurring errors (a faulty ultrasonic sensor) and simplifying model assumptions (e.g. usage of two identical motors). We show that our model-based approach is able to perform reasonably well even under these extreme circumstances.

Assistive Robotic Technologies for Next-Generation Smart Wheelchairs: Codesign and Modularity to Improve Users’ Quality of Life

“…Finally, a single-board computer (Raspberry Pi 3B+) acts as the ROS master, using a publisher-subscriber model. We refer the reader to [16] for more details on all these components, including the schematics of the hardware architecture. The wheeled platform developed at the Institut National des Sciences Appliquées (INSA) Rennes also builds on the Quickie Salsa M 2 wheelchair.…”

“…It consists of four blocks, where some are deterministic and others are stochastic. First, the wheelchair dynamics block includes the physical equations of motion of a two-wheeled differential drive vehicle (for the experimental model identification, see [16]). Second, the environment block is used to model the static obstacles, with the vehicle having limited knowledge of the global map.…”

AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

“…• eq. ( 1b) is that each motor servo-drive (power module) includes a low-level safety functionality which bypasses (overrides) any requested control action beyond v max > 0 and below v min < 0. physically-inspired experimentally identified parameters [30] α b field of view (sector area) of sensors detecting obstacles in Fig. 1 rad…”