• We present a new robot pose prediction method for static stability estimation. • The method approximates the terrain by least-squares planes to reduce the runtime. • A stochastic version accounts for noise in the robot state and the terrain model. • We systematically compared it with a physics simulation in many distinct scenarios. • The new method is significantly faster and competitive in realistic situations. a r t i c l e i n f o  Due to the advancements of robotic systems, they are able to be employed in more unstructured outdoor environments. In such environments the robot-terrain interaction becomes a highly non-linear function. Several methods were proposed to estimate the robot-terrain interaction: machine learning methods, iterative geometric methods, quasi-static and fully dynamic physics simulations. However, to the best of our knowledge there has been no systematic evaluation comparing those methods. In this paper, we present a newly developed iterative contact point estimation method for static stability estimation of actively reconfigurable robots. This new method is systematically compared to a physics simulation in a comprehensive evaluation. Both interaction models determine the contact points between robot and terrain and facilitate a subsequent static stability prediction. Hence, they can be used in our state space global planner for rough terrain to evaluate the robot's pose and stability. The analysis also compares deterministic versions of both methods to stochastic versions which account for uncertainty in the robot configuration and the terrain model. The results of this analysis show that the new iterative method is a valid and fast approximate method. It is significantly faster compared to a physics simulation while providing good results in realistic robotic scenarios.
Finally, in REF an iterative contact point estimation method for estimating the stability of actively reconfigurable robots was presented.
Design and comparative evaluation of an iterative contact point estimation method for static stability estimation of mobile actively reconfigurable robots
Robotics Auton. Syst.
Robotics Auton. Syst.
Computer Science