Stochastic Modeling of Distance to Collision for Robot Manipulators

by   Nikhil Das, et al.

Evaluating distance to collision for robot manipulators is useful for assessing the feasibility of a robot configuration or for defining safe robot motion in unpredictable environments. However, distance estimation is a timeconsuming operation, and the sensors involved in measuring the distance are always noisy. A challenge thus exists in evaluating the expected distance to collision for safer robot control and planning. In this work, we propose the use of Gaussian process (GP) regression and the forward kinematics (FK) kernel (a similarity function for robot manipulators) to efficiently and accurately estimate distance to collision. We show that the GP model with the FK kernel achieves 70 times faster distance evaluations compared to a standard geometric technique, and up to 13 times more accurate evaluations compared to other regression models, even when the GP is trained on noisy distance measurements. We employ this technique in trajectory optimization tasks and observe 9 times faster optimization than with the noise-free geometric approach yet obtain similar optimized motion plans. We also propose a confidence-based hybrid model that uses model-based predictions in regions of high confidence and switches to a more expensive sensor-based approach in other areas, and we demonstrate the usefulness of this hybrid model in an application involving reaching into a narrow passage.


page 1

page 4


Forward Kinematics Kernel for Improved Proxy Collision Checking

Kernel functions may be used in robotics for comparing different poses o...

Collision-free Motion Generation Based on Stochastic Optimization and Composite Signed Distance Field Networks of Articulated Robot

Safe robot motion generation is critical for practical applications from...

Learning-Based Proxy Collision Detection for Robot Motion Planning Applications

This paper demonstrates that collision detection-intensive applications ...

Chance-Constrained Motion Planning using Modeled Distance-to-Collision Functions

This paper introduces Chance Constrained Gaussian Process-Motion Plannin...

Proprioceptive Robot Collision Detection through Gaussian Process Regression

This paper proposes a proprioceptive collision detection algorithm based...

Uncertainty-aware Safe Exploratory Planning using Gaussian Process and Neural Control Contraction Metric

In this paper, we consider the problem of using a robot to explore an en...

Please sign up or login with your details

Forgot password? Click here to reset