<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jiazhi Song</style></author><author><style face="normal" font="default" size="100%">Antoine Petraki</style></author><author><style face="normal" font="default" size="100%">Brandon J. DeHart</style></author><author><style face="normal" font="default" size="100%">Inna Sharf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chance-Constrained Rollover-Free Manipulation Planning With Uncertain Payload Mass</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE/ASME Transactions on Mechatronics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1109/TMECH.2023.3333793</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This article presents a chance-constrained rollover-free manipulation planning method for robotic arms under payload mass uncertainty. The corresponding motion planning problem is stated as a chance-constrained nonlinear optimal control problem (NOCP) subject to kinematics and rollover stability constraints. The latter takes the form of a chance constraint that ensures a certain probability of the robot maintaining dynamic rollover stability in the presence of payload mass uncertainty. To achieve efficient solutions to the NOCP, a novel geometric bound for the stability region is derived. The novel bound is then utilized to modify the rollover-stability constraint. To showcase its benefit, comparisons between the proposed bound of the probabilistic rollover-stability measure and the naive noise model are provided through statistical analysis. The formulation's practicality is demonstrated through experiments with a Kinova Jaco2 arm mounted on a free-to-rollover platform. Results demonstrate greater robustness of the robot's motion plan to mass uncertainty and computational efficiency of the trajectory generation.</style></abstract></record></records></xml>