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

Rough-terrain mobile robot planning and control with application to planetary exploration

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2001.Includes bibliographical references (leaves 119-130).Future planetary exploration missions will require mobile robots to perform difficult tasks in highly challenging terrain, with limited human supervision. Current motion planning and control algorithms are not well suited to rough-terrain mobility, since they generally do not consider the physical characteristics of the rover and its environment. Failure to understand these characteristics could lead to rover entrapment and mission failure. In this thesis, methods are presented for improved rough-terrain mobile robot mobility, which exploit fundamental physical models of the rover and terrain. Wheel-terrain interaction has been shown to be critical to rough terrain mobility. A wheel-terrain interaction model is presented, and a method for on-line estimation of important model parameters is proposed. The local terrain profile also strongly influences robot mobility. A method for on-line estimation of wheel-terrain contact angles is presented. Simulation and experimental results show that wheel-terrain model parameters and contact angles can be estimated on-line with good accuracy. Two rough-terrain planning algorithms are introduced. First, a motion planning algorithm is presented that is computationally efficient and considers uncertainty in rover sensing and localization. Next, an algorithm for geometrically reconfiguring the rover kinematic structure to optimize tipover stability margin is presented. Both methods utilize models developed earlier in the thesis.(cont.) Simulation and experimental results on the Jet Propulsion Laboratory Sample Return Rover show that the algorithms allow highly stable, semi-autonomous mobility in rough terrain. Finally, a rough-terrain control algorithm is presented that exploits the actuator redundancy found in multi-wheeled mobile robots to improve ground traction and reduce power consumption. The algorithm uses models developed earlier in the thesis. Simulation and experimental results show that the algorithm leads to improved wheel thrust and thus increased mobility in rough terrain.by Karl David Iagnemma.Ph.D

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Methods for the improvement of power resource prediction and residual range estimation for offroad unmanned ground vehicles

Unmanned Ground Vehicles (UGVs) are becoming more widespread in their deployment. Advances in technology have improved not only their reliability but also their ability to perform complex tasks. UGVs are particularly attractive for operations that are considered unsuitable for human operatives. These include dangerous operations such as explosive ordnance disarmament, as well as situations where human access is limited including planetary exploration or search and rescue missions involving physically small spaces. As technology advances, UGVs are gaining increased capabilities and consummate increased complexity, allowing them to participate in increasingly wide range of scenarios. UGVs have limited power reserves that can restrict a UGV’s mission duration and also the range of capabilities that it can deploy. As UGVs tend towards increased capabilities and complexity, extra burden is placed on the already stretched power resources. Electric drives and an increasing array of processors, sensors and effectors, all need sufficient power to operate. Accurate prediction of mission power requirements is therefore of utmost importance, especially in safety critical scenarios where the UGV must complete an atomic task or risk the creation of an unsafe environment due to failure caused by depleted power. Live energy prediction for vehicles that traverse typical road surfaces is a wellresearched topic. However, this is not sufficient for modern UGVs as they are required to traverse a wide variety of terrains that may change considerably with prevailing environmental conditions. This thesis addresses the gap by presenting a novel approach to both off and on-line energy prediction that considers the effects of weather conditions on a wide variety of terrains. The prediction is based upon nonlinear polynomial regression using live sensor data to improve upon the accuracy provided by current methods. The new approach is evaluated and compared to existing algorithms using a custom ‘UGV mission power’ simulation tool. The tool allows the user to test the accuracy of various mission energy prediction algorithms over a specified mission routes that include a variety of terrains and prevailing weather conditions. A series of experiments that test and record the ‘real world’ power use of a typical small electric drive UGV are also performed. The tests are conducted for a variety of terrains and weather conditions and the empirical results are used to validate the results of the simulation tool. The new algorithm showed a significant improvement compared with current methods, which will allow for UGVs deployed in real world scenarios where they must contend with a variety of terrains and changeable weather conditions to make accurate energy use predictions. This enables more capabilities to be deployed with a known impact on remaining mission power requirement, more efficient mission durations through avoiding the need to maintain excessive estimated power reserves and increased safety through reduced risk of aborting atomic operations in safety critical scenarios. As supplementary contribution, this work created a power resource usage and prediction test bed UGV and resulting data-sets as well as a novel simulation tool for UGV mission energy prediction. The tool implements a UGV model with accurate power use characteristics, confirmed by an empirical test series. The tool can be used to test a wide variety of scenarios and power prediction algorithms and could be used for the development of further mission energy prediction technology or be used as a mission energy planning tool

Study of Mobile Robot Operations Related to Lunar Exploration

Mobile robots extend the reach of exploration in environments unsuitable, or unreachable, by humans. Far-reaching environments, such as the south lunar pole, exhibit lighting conditions that are challenging for optical imagery required for mobile robot navigation. Terrain conditions also impact the operation of mobile robots; distinguishing terrain types prior to physical contact can improve hazard avoidance. This thesis presents the conclusions of a trade-off that uses the results from two studies related to operating mobile robots at the lunar south pole. The lunar south pole presents engineering design challenges for both tele-operation and lidar-based autonomous navigation in the context of a near-term, low-cost, short-duration lunar prospecting mission. The conclusion is that direct-drive tele-operation may result in improved science data return. The first study is on demonstrating lidar reflectance intensity, and near-infrared spectroscopy, can improve terrain classification over optical imagery alone. Two classification techniques, Naive Bayes and multi-class SVM, were compared for classification errors. Eight terrain types, including aggregate, loose sand and compacted sand, are classified using wavelet-transformed optical images, and statistical values of lidar reflectance intensity. The addition of lidar reflectance intensity was shown to reduce classification errors for both classifiers. Four types of aggregate material are classified using statistical values of spectral reflectance. The addition of spectral reflectance was shown to reduce classification errors for both classifiers. The second study is on human performance in tele-operating a mobile robot over time-delay and in lighting conditions analogous to the south lunar pole. Round-trip time delay between operator and mobile robot leads to an increase in time to turn the mobile robot around obstacles or corners as operators tend to implement a `wait and see\u27 approach. A study on completion time for a cornering task through varying corridor widths shows that time-delayed performance fits a previously established cornering law, and that varying lighting conditions did not adversely affect human performance. The results of the cornering law are interpreted to quantify the additional time required to negotiate a corner under differing conditions, and this increase in time can be interpreted to be predictive when operating a mobile robot through a driving circuit

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