3D-position tracking and control for all-terrain robots

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010195495	TJ211.415 L36 2008	Open Access Book	Book	Searching... Unknown
Searching... PSZ KL	33000000010062	TJ211.415 L36 2008	Open Access Book	Book	Searching... Unknown

Rough terrain robotics is a fast evolving field of research and a lot of effort is deployed towards enabling a greater level of autonomy for outdoor vehicles. This book demonstrates how the accuracy of 3D position tracking can be improved by considering rover locomotion in rough terrain as a holistic problem. Although the selection of appropriate sensors is crucial to accurately track the rover's position, it is not the only aspect to consider. Indeed, the use of an unadapted locomotion concept severely affects the signal to noise ratio of the sensors, which leads to poor motion estimates. In this work, a mechanical structure allowing smooth motion across obstacles with limited wheel slip is used. In particular, it enables the use of odometry and inertial sensors to improve the position estimation in rough terrain. A method for computing 3D motion increments based on the wheel encoders and chassis state sensors is developed. Because it accounts for the kinematics of the rover, this method provides better results than the standard approach. To further improve the accuracy of the position tracking and the rover's climbing performance, a controller minimizing wheel slip is developed. The algorithm runs online and can be adapted to any kind of passive wheeled rover. Finally, sensor fusion using 3D-Odometry, inertial sensors and visual motion estimation based on stereovision is presented. The experimental results demonstrate how each sensor contributes to increase the accuracy and robustness of the 3D position estimation.

1 Introduction	p. 1
1.1 Autonomy in Rough Terrain	p. 1
1.2 The Open Challenges of Rough Terrain Navigation	p. 2
1.2.1 Lack of Prior Information	p. 2
1.2.2 Perception	p. 2
1.2.3 Locomotion	p. 2
1.3 Research Context and Scope	p. 3
1.4 Structure of the Book	p. 4
2 The SOLERO Rover	p. 7
2.1 Mechanical Design of SOLERO	p. 7
2.2 Control Architecture	p. 11
2.2.1 Sensors and Actuators	p. 11
2.2.2 Software Architecture	p. 17
2.3 Summary	p. 19
3 3D-Odometry	p. 21
3.1 3D-Odometry	p. 21
3.1.1 Bogie Displacement	p. 22
3.1.2 3D Displacement	p. 25
3.1.3 Contact Angles Estimation	p. 27
3.2 Experimental Results	p. 27
3.3 Summary	p. 32
4 Control in Rough-Terrain	p. 33
4.1 Quasi-static Model of a Wheeled Rover	p. 34
4.1.1 Mobility Analysis	p. 34
4.1.2 A 3D Static Model	p. 36
4.2 Torque Optimization	p. 36
4.2.1 Wheel Slip Model	p. 37
4.2.2 Optimization Algorithm	p. 38
4.2.3 Torque Optimization for SOLERO	p. 41
4.3 Rover Motion	p. 43
4.4 Experimental Results	p. 45
4.4.1 Simulation Tools	p. 45
4.4.2 Experiments	p. 46
4.5 Wheel-Ground Contact Angles	p. 49
4.6 Summary	p. 50
5 Position Tracking in Rough-Terrain	p. 53
5.1 Sensor Selection for Motion Perception	p. 53
5.2 Uncertainties Propagation	p. 56
5.2.1 Coordinate Systems and Transformations	p. 56
5.2.2 Error Propagation	p. 57
5.3 Sensor Fusion	p. 58
5.3.1 Sensor Models	p. 60
5.3.2 State Prediction Model	p. 63
5.4 Experimental Results	p. 65
5.4.1 Inertial Sensor and 3D-Odometry	p. 65
5.4.2 Enhancement with VME	p. 73
5.5 Summary	p. 78
6 Conclusion	p. 81
A Kinematic and Quasi-static Model of SOLERO	p. 83
A.1 Kinematic Model	p. 83
A.1.1 The Bogies	p. 84
A.1.2 The Main Body	p. 85
A.1.3 The Front Fork	p. 85
A.2 The Quasi-static Model of SOLERO	p. 86
A.2.1 Linear Dependence of the Wheel Torques	p. 89
A.2.2 Equal Torque Solution	p. 90
B Linearized Models	p. 91
B.1 Accelerometers Model	p. 91
B.2 Gyroscopes State Transition	p. 92
C The Gauss-Markov Process	p. 93
D Visual Motion Estimation	p. 97
References	p. 99
Index	p. 105

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