Terrain sensing and estimation for dynamic outdoor mobile robots

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (p. 120-125).In many applications, mobile robots are required to travel on outdoor terrain at high speed. Compared to traditional low-speed, laboratory-based robots, outdoor scenarios pose increased perception and mobility challenges which must be considered to achieve high performance. Additionally, high-speed driving produces dynamic robot-terrain interactions which are normally negligible in low speed driving. This thesis presents algorithms for estimating wheel slip and detecting robot immobilization on outdoor terrain, and for estimating traversed terrain profile and classifying terrain type. Both sets of algorithms utilize common onboard sensors. Two methods are presented for robot immobilization detection. The first method utilizes a dynamic vehicle model to estimate robot velocity and explicitly estimate longitudinal wheel slip. The vehicle model utilizes a novel simplified tire traction/braking force model in addition to estimating external resistive disturbance forces acting on the robot. The dynamic model is combined with sensor measurements in an extended Kalman filter framework. A preliminary algorithm for adapting the tire model parameters is presented. The second, model-free method takes a signal recognition-based approach to analyze inertial measurements to detect robot immobilization. Both approaches are experimentally validated on a robotic platform traveling on a variety of outdoor terrains. Two detector fusion techniques are proposed and experimentally validated which combine multiple detectors to increase detection speed and accuracy. An algorithm is presented to classify outdoor terrain for high-speed mobile robots using a suspension mounted accelerometer. The algorithm utilizes a dynamic vehicle model to estimate the terrain profile and classifies the terrain based on spatial frequency components of the estimated profile. The classification algorithm is validated using experimental results collected with a commercial automobile driving in real-world conditions.by Christopher Charles Ward.S.M

Slide-Down Prevention for Wheeled Mobile Robots on Slopes

Wheeled mobile robots on inclined terrain can slide down due to loss of traction and gravity. This type of instability, which is different from tip-over, can provoke uncontrolled motion or get the vehicle stuck. This paper proposes slide-down prevention by real-time computation of a straightforward stability margin for a given ground-wheel friction coefficient. This margin is applied to the case study of Lazaro, a hybrid skid-steer mobile robot with caster-leg mechanism that allows tests with four or five wheel contact points. Experimental results for both ADAMS simulations and the actual vehicle demonstrate the effectiveness of the proposed approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Distributed active traction control system applied to the RoboCup middle size league

This work addresses the problem of traction control in mobile wheeled robots in the particular case of the RoboCup Middle Size League (MSL). The slip control problem is formulated using simple friction models for ISePorto Team robots with a differential wheel configuration. Traction was also characterized experimentally in the MSL scenario for relevant game events. This work proposes a hierarchical traction control architecture which relies in local slip detection and control at each wheel, with relevant information being relayed to a higher level responsible for global robot motion control. A dedicated one axis control embedded hardware subsystem allowing complex local control, high frequency current sensing and odometric information procession was developed. This local axis control board is integrated in a distributed system using CAN bus communications. The slipping observer was implemented in the axis control hardware nodes integrated in the ISePorto robots and was used to control and detect loss of for traction. %and to detect the ball in the kicking device. An external vision system was used to perform a qualitative analysis of the slip detection and observer performance results are presented

Trajectory tracking and traction coordinating controller design for lunar rover based on dynamics and kinematics analysis

Trajectory tracking control is a necessary part for autonomous navigation of planetary rover and traction coordinating control can reduce the forces consumption during navigation. As a result, a trajectory tracking and traction coordinating controller for wheeled lunar rover with Rocker Bogie is proposed in the paper. Firstly, the longitudinal dynamics model and the kinematics model of six-wheeled rover are established. Secondly, the traction coordinating control algorithm is studied based on sliding mode theory with improved exponential approach law. Thirdly, based on kinematics analysis and traction system identification, the trajectory tracking controller is designed using optimal theory. Then, co-simulations between ADAMS and MATLAB/Simulink are carried out to validate the proposed algorithm, and the simulation results have confirmed the effectiveness of path tracking and traction mobility improving

Infrastructure-Aided Localization and State Estimation for Autonomous Mobile Robots

A slip-aware localization framework is proposed for mobile robots experiencing wheel slip in dynamic environments. The framework fuses infrastructure-aided visual tracking data (via fisheye lenses) and proprioceptive sensory data from a skid-steer mobile robot to enhance accuracy and reduce variance of the estimated states. The slip-aware localization framework includes: the visual thread to detect and track the robot in the stereo image through computationally efficient 3D point cloud generation using a region of interest; and the ego motion thread which uses a slip-aware odometry mechanism to estimate the robot pose utilizing a motion model considering wheel slip. Covariance intersection is used to fuse the pose prediction (using proprioceptive data) and the visual thread, such that the updated estimate remains consistent. As confirmed by experiments on a skid-steer mobile robot, the designed localization framework addresses state estimation challenges for indoor/outdoor autonomous mobile robots which experience high-slip, uneven torque distribution at each wheel (by the motion planner), or occlusion when observed by an infrastructure-mounted camera. The proposed system is real-time capable and scalable to multiple robots and multiple environmental cameras

Methods for Wheel Slip and Sinkage Estimation in Mobile Robots

Future outdoor mobile robots will have to explore larger and larger areas, performing difficult tasks, while preserving, at the same time, their safety. This will primarily require advanced sensing and perception capabilities. Video sensors supply contact-free, precise measurements and are flexible devices that can be easily integrated with multi-sensor robotic platforms. Hence, they represent a potential answer to the need of new and improved perception capabilities for autonomous vehicles. One of the main applications of vision in mobile robotics is localization. For mobile robots operating on rough terrain, conventional dead reckoning techniques are not well suited, since wheel slipping, sinkage, and sensor drift may cause localization errors that accumulate without bound during the vehicle’s travel. Conversely, video sensors are exteroceptive devices, that is, they acquire information from the robot’s environment; therefore, vision-based motion estimates are independent of the knowledge of terrain properties and wheel-terrain interaction. Indeed, like dead reckoning, vision could lead to accumulation of errors; however, it has been proved that, compared to dead reckoning, it allows more accurate results and can be considered as a promising solution to the problem of robust robot positioning in high-slip environments. As a consequence, in the last few years, several localization methods using vision have been developed. Among them, visual odometry algorithms, based on the tracking of visual features over subsequent images, have been proved particularly effective. Accurate and reliable methods to sense slippage and sinkage are also desirable, since these effects compromise the vehicle’s traction performance, energy consumption and lead to gradual deviation of the robot from the intended path, possibly resulting in large drift and poor results of localization and control systems. For example, the use of conventional dead-reckoning technique is largely compromised, since it is based on the assumption that wheel revolutions can be translated into correspondent linear displacements. Thus, if one wheel slips, then the associated encoder will register revolutions even though these revolutions do not correspond to a linear displacement of the wheel. Conversely, if one wheel skids, fewer encoder pulses will be counted. Slippage and sinkage measurements are also valuable for terrain identification according to the classical terramechanics theory. This chapter investigates vision-based onboard technology to improve mobility of robots on natural terrain. A visual odometry algorithm and two methods for online measurement of vehicle slip angle and wheel sinkage, respectively, are discussed. Tests results are presented showing the performance of the proposed approaches using an all-terrain rover moving across uneven terrain

Fully Proprioceptive Slip-Velocity-Aware State Estimation for Mobile Robots via Invariant Kalman Filtering and Disturbance Observer

This paper develops a novel slip estimator using the invariant observer design theory and Disturbance Observer (DOB). The proposed state estimator for mobile robots is fully proprioceptive and combines data from an inertial measurement unit and body velocity within a Right Invariant Extended Kalman Filter (RI-EKF). By embedding the slip velocity into $\mathrm{SE}_3(3)$ Lie group, the developed DOB-based RI-EKF provides real-time accurate velocity and slip velocity estimates on different terrains. Experimental results using a Husky wheeled robot confirm the mathematical derivations and show better performance than a standard RI-EKF baseline. Open source software is available for download and reproducing the presented results.Comment: github repository at https://github.com/UMich-CURLY/slip_detection_DOB. arXiv admin note: text overlap with arXiv:1805.10410 by other author

DEVELOPMENT AND EVALUATION OF AN ADVANCED REAL-TIME ELECTRICAL POWERED WHEELCHAIR CONTROLLER

Advances in Electric Powered Wheelchairs (EPW) have improved mobility for people with disabilities as well as older adults, and have enhanced their integration into society. Some of the issues still present in EPW lie in the difficulties when encountering different types of terrain, and access to higher or low surfaces. To this end, an advanced real-time electrical powered wheelchair controller was developed. The controller was comprised of a hardware platform with sensors measuring the speed of the driving, caster wheels and the acceleration, with a single board computer for implementing the control algorithms in real-time, a multi-layer software architecture, and modular design. A model based real-time speed and traction controller was developed and validated by simulation. The controller was then evaluated via driving over four different surfaces at three specified speeds. Experimental results showed that model based control performed best on all surfaces across the speeds compared to PID (proportional-integral-derivative) and Open Loop control. A real-time slip detection and traction control algorithm was further developed and evaluated by driving the EPW over five different surfaces at three speeds. Results showed that the performance of anti-slip control was consistent on the varying surfaces at different speeds. The controller was also tested on a front wheel drive EPW to evaluate a forwarding tipping detection and prevention algorithm. Experimental results showed that the tipping could be accurately detected as it was happening and the performance of the tipping prevention strategy was consistent on the slope across different speeds. A terrain-dependent EPW user assistance system was developed based on the controller. Driving rules for wet tile, gravel, slopes and grass were developed and validated by 10 people without physical disabilities. The controller was also adapted to the Personal Mobility and Manipulation Appliance (PerMMA) Generation II, which is an advanced power wheelchair with a flexible mobile base, allowing it to adjust the positions of each of the four casters and two driving wheels. Simulations of the PerMMA Gen II system showed that the mobile base controller was able to climb up to 8” curb and maintain passenger’s posture in a comfort position