AB  - Stability prediction is an important concern for mobile robots operating in rough environments. Having the capacity to predict areas of instability means pro-actively being able to plan safer traversable paths. The most influential tip-over stability measures are based on two criteria, the robot's center of mass (CM) and the supporting polygon (SP) defined by the convex area spanned between the ground contact-points. However, there is significant uncertainty associated with many parameters in the planning pipe-line: the actual robot kino-dynamic model, its localisation in the ground, and the terrain models, particularly in uneven terrain. This article proposes a statistical analysis of stability prediction to account for some of the uncertainties. This is accomplished using the force angle (FA) stability measure for a reconfigurable multi-tracked vehicle fitted with flippers, a manipulator arm and a sensor head. Probability density function (PDF) of contact-points, CM and the FA stability measure are numerically estimated, with simulation results performed on the open dynamics engine (ODE) simulator based on uncertain parameters. Two techniques are presented: a conventional Monte Carlo scheme, and a structured unscented transform (UT) which results in significant improvement in computational efficiency. Experimental results on maps obtained from a range camera fitted on the sensor head while the robot traverses over a ramp and a series of steps are presented that confirms the validity of the proposed probabilistic stability prediction method. © 2013 IEEE.
AU  - Norouzi, M
AU  - Miro, JV
AU  - Dissanayake, G
DA  - 2013/11/14
DO  - 10.1109/ICRA.2013.6630575
EP  - 196
JO  - Proceedings - IEEE International Conference on Robotics and Automation
PY  - 2013/11/14
SP  - 191
TI  - A statistical approach for uncertain stability analysis of mobile robots
Y1  - 2013/11/14
Y2  - 2026/05/21
ER  -