By Pierre Lamon
Rough terrain robotics is a quick evolving box of study and many attempt is deployed in the direction of permitting a better point of autonomy for outside cars. This publication demonstrates how the accuracy of 3D place monitoring may be more suitable by way of contemplating rover locomotion in tough terrain as a holistic challenge. even supposing the choice of applicable sensors is essential to correctly music the rover’s place, it isn't the one point to contemplate. certainly, using an unadapted locomotion inspiration critically impacts the sign to noise ratio of the sensors, which results in negative movement estimates. during this paintings, a mechanical constitution permitting soft movement throughout hindrances with restricted wheel slip is used. particularly, it permits using odometry and inertial sensors to enhance the placement estimation in tough terrain. a mode for computing 3D movement increments in response to the wheel encoders and chassis nation sensors is built. since it debts for the kinematics of the rover, this system presents greater effects than the traditional process. To extra enhance the accuracy of the placement monitoring and the rover’s hiking functionality, a controller minimizing wheel slip is built. The set of rules runs on-line and will be tailored to any type of passive wheeled rover. eventually, sensor fusion utilizing 3D-Odometry, inertial sensors and visible movement estimation in response to stereovision is gifted. The experimental effects reveal how every one sensor contributes to extend the accuracy and robustness of the 3D place estimation.
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Additional info for 3D-Position Tracking and Control for All-Terrain Robots
In some cases, the rover was unable to even climb some obstacles and to reach the ﬁnal position when driven using the reactive approach. 1. It can be seen that the integrated wheel slip is smaller if predictive control is used. All the results obtained during the experiments are similar to the one depicted in Figs. 14. On ﬂat terrain the performance of both controllers are identical and wheel slip is zero. 1. 5 2 x [m] Fig. 13. Total slip and rear-wheel slip for experiment 1 (total slip is scaled by a factor of 500).
44 Control in Rough-Terrain Fig. 9. Images of the Mars surface taken by the NASA rover Spirit next to the Bonneville Crater In practice, parameterized wheel-soil interaction models cannot be used for such terrain types. Thus, rolling resistance cannot be derived from the models and must be estimated as the robot moves. Our approach introduces a closedloop controller for estimating rolling resistance and minimizing wheel slip. It is depicted in Fig. 10 Vd + − Vr PID N Model & Optimization Mc Correction Distribution Vd Vr Mr Mc desired rover velocity measured rover velocity rolling resistance torque (unknown) global correction torque Mw + Mo N s Mw s Terrain Mr Mo + Rover optimal torques normal forces rover state wheel correction torques Fig.
The diﬀerence gets bigger as the rover encounters rough terrain. At the end of the experiment, the predictive controller performs 16% better than the reactive controller. Wheel-Ground Contact Angles 49 peaks are at the same places for both controllers, but the amplitude is smaller for the predictive controller. That means that when a wheel slips at a given place, it slips less when predictive control is used. This major behavior can be observed in Figs. 14 when looking closely at curves 1 and 2.
3D-Position Tracking and Control for All-Terrain Robots by Pierre Lamon