Path-Following Control of Wheeled Planetary Exploration Robots Moving on Deformable Rough Terrain

The control of planetary rovers, which are high performance mobile robots that move on deformable rough terrain, is a challenging problem. Taking lateral skid into account, this paper presents a rough terrain model and nonholonomic kinematics model for planetary rovers. An approach is proposed in which the reference path is generated according to the planned path by combining look-ahead distance and path updating distance on the basis of the carrot following method. A path-following strategy for wheeled planetary exploration robots incorporating slip compensation is designed. Simulation results of a four-wheeled robot on deformable rough terrain verify that it can be controlled to follow a planned path with good precision, despite the fact that the wheels will obviously skid and slip

A Modelica Library to Add Contact Dynamics and Terramechanics to Multi-Body Mechanics

The Contact Dynamics library extends the multi-body Modelica Standard Library with contact calculation to the environment, namely soft soil and hard obstacles. A focus is on terramechanics, i. e. wheels driving on soft and dry soil, and a handful of models are implemented. Additionally, a Hertz contact model for hard and elastic contact, between bodies themselves or to obstacles in the environment (e. g. rocks in the soft soil), is available as well. The capabilities of the library have been key in the development of rovers for planetary exploration such as the upcoming MMX mission to the Martian moon Phobos

Tractable robot simulation for terrain leveling

This thesis describes the problem of terrain leveling, in which one or more robots or vehicles are used to atten a terrain. The leveling operation is carried out either in preparation for construction, or for terrain reparation. In order to develop and prototype such a system, the use of simulation is advantageous. Such a simulation requires high fidelity to accurately model earth moving robots, which navigate uneven terrain and potentially manipulate the terrain itself. It has been found that existing tools for robot simulation typically do not adequately model deformable and/or uneven terrain. Software which does exist for this purpose, based on a traditional physics engine, is difficult if not impossible to run in real-time while achieving the desired accuracy. A number of possible approaches are proposed for a terrain leveling system using autonomous mobile robots. In order to test these approaches in simulation, a 2D simulator called Alexi has been developed, which uses the predictions of a neural network rather than physics simulation, to predict the motion of a vehicle and changes to a terrain. The neural network is trained using data captured from a high-fidelity non-real-time 3D simulator called Sandbox. Using a trained neural network to drive the 2D simulation provides considerable speed-up over the high-fidelity 3D simulation, allowing behaviour to be simulated in real-time while still capturing the physics of the agents and the environment. Two methods of simulating terrain in Sandbox are explored with results related to performance given for each. Two variants of Alexi are also explored, with results related to neural network training and generalization provided

Analysis of inverse simulation algorithms with an application to planetary rover guidance and control

Rover exploration is a contributing factor to driving the relevant research forward on guidance, navigation, and control (GNC). Yet, there is a need for incorporating the dynamic model into the controller for increased accuracy. Methods that use the model are limited by issues such as linearity, systems affine in the control, number of inputs and outputs. Inverse Simulation is a more general approach that uses a mathematical model and a numerical scheme to calculate the control inputs necessary to produce a desired response defined using the output variables. This thesis develops the Inverse Simulation algorithm for a general state space model and utilises a numerical Newton-Raphson scheme to converge to the inputs using two approaches: The Differentiation method converges based on the state and output equations. The Integration method converges based on whether the output matches the desired and is suitable for grey or black-box models. The thesis offers extensive insights into the requirements and application of Inverse Simulation and the performance parameters. Attention is given to how the inputs and outputs affect the Jacobian formulation and ensure an efficient solution. The linear case and the relationship with feedback linearisation are examined. Examples are given using simple mechanical systems and an example is also given as to how Inverse Simulation can be used for determining system input disturbances. Inverse Simulation is applied for the first time for guidance and control of a fourwheeled, differentially driven rover. The desired output is the time history of the desired trajectory and is used to produce the required control inputs. The control inputs are nominal and are applied to the rover without additional correction. Using insights from the system’s physics and actuation, the Differentiation and Integration schemes are developed based on the general method presented in this thesis. The novel Differentiation scheme employs a non-square Jacobian. The method provides very accurate position and orientation control of the rover while considering the limitations of the model used. Finally, the application of Inverse Simulation to the rover is supported by a review of current designs that resulted in a rover taxonomy

Adaptive Sliding Mode Control of Mobile Manipulators with Markovian Switching Joints

The hybrid joints of manipulators can be switched to either active (actuated) or passive (underactuated) mode as needed. Consider the property of hybrid joints, the system switches stochastically between active and passive systems, and the dynamics of the jump system cannot stay on each trajectory errors region of subsystems forever; therefore, it is difficult to determine whether the closed-loop system is stochastically stable. In this paper, we consider stochastic stability and sliding mode control for mobile manipulators using stochastic jumps switching joints. Adaptive parameter techniques are adopted to cope with the effect of Markovian switching and nonlinear dynamics uncertainty and follow the desired trajectory for wheeled mobile manipulators. The resulting closed-loop system is bounded in probability and the effect due to the external disturbance on the tracking errors can be attenuated to any preassigned level. It has been shown that the adaptive control problem for the Markovian jump nonlinear systems is solvable if a set of coupled linear matrix inequalities (LMIs) have solutions. Finally, a numerical example is given to show the potential of the proposed techniques