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Robotic capture using DEOS

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Introduction: Present day challenges

e.Deorbit clamping the target

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• Robustness against measurement noise and outliers for smooth control in presence of orbital

disturbances

• Allowing Visual servoing during times of occlusion and bad lighting conditions

• Parameter Estimation (geometry/inertial)

• Long-term Prediction of relative trajectory parameters

• Framework for Multiple Sensor Fusion by using measurements from several sensors

Introduction : Need for estimator

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Pose estimates of Target in camera frame

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Introduction: Need for estimator

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Simulated scenario of the grasping problem at DLR's

OOS-simulator Body diagrams of servicer and tumbling

target

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• Quaternion kinematics

• Newton-Euler equations

• Hill-Clohessy-Wiltshire equations

Introduction : Modeling

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Introduction : Visual servoing

Visual servoing on the On-Orbit Servicer

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• Larger Sampling intervals

• Higher presence of Outliers (not Gaussian)

• Varying noise characteristics due to changing lighting conditions and occlusion

• Due to delay in image-processing, the recent available measurement is a snapshot of a past state

Introduction : Vision System Characteristics

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• A Bayesian approach is used

• Variational Bayesian inferencing is used to approximate posterior distributions

• An Inv-Gamma distribution is assumed on diagonal elements

Estimation (new approaches) : Adaptive EKF

• Algorithm-1 is an iterative scheme which adapts in 3-4 steps

• Minimize Kullback-Leibler (KL) divergence to obtain iterative formulation

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Estimation (new approaches) : Outlier Rejection

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Estimation (new approaches) : Out-of-Sequence Measurements

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Estimation (new approaches) : Visual Servoing

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Estimated and Measured pose

Results : EKF

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Velocities and Inertia

Results : EKF

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Grasping point’s pose (estimated)

Results : EKF

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Pose of target center of mass w.r.t servicer’s center of mass

Results : EKF

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Results : EKF

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Results : EKF

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Results : EKF (OOSM)

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Results : EKF

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• Smooth Visual Servoing is possible due to filtered estimates of motion parameters

• Vision sensor issues were handled with computationally efficient algorithms for Parameter Estimation

• Model developed to incorporate Robot kinematics for PBVS

• With OOSM and Parallel updates, multiple sensor fusion can be used to improve estimates

• Subsequent research naturally lends towards evaluation of the proposed estimator with DLR-OOS data.

Conclusion

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