Centrala begrepp
Utilizing neural implicit swept volume models can significantly speed up collision detection in robotics applications.
Sammanfattning
The article introduces a novel approach using neural implicit swept volume models to enhance collision detection in robotics. By leveraging deep learning techniques, the authors aim to accelerate collision detection and motion planning processes. The proposed model predicts signed distances for any point in the task space to the robot motion's swept volume, enabling efficient collision checks. The algorithm combines deep learning-based signed distance computations with geometric collision checkers to ensure accuracy while improving speed. Experimental validation in simulated and real-world robotic experiments demonstrates the effectiveness of the approach in enhancing a commercial bin picking application. The study highlights the potential of neural networks to streamline collision detection processes in various robotic applications.
Statistik
"We use 60 % of the data for training, 25 % for validation, and 15 % for testing."
"For each scene, we sample a single start configuration uniformly from within the joint limits, and 100 random goal configurations."
"The network achieves a classification accuracy of 93.1 %, with 6.3 % false positives, and 0.6 % false negatives."
"Using a safety margin of 5 mm to classify signed distances into collisions and non-collisions."
"The average time for checking a given motion is 28 ±4 ms for the GCC and 0.70 ms for the network."
Citat
"We propose an implicit representation based on a deep neural network that learns the function (x, q0, q1) → δ."
"Our networks are composed of nb blocks of equal dimension ndim."
"The algorithm effectively reduces the average number of exact collision checks that need to be executed before finding the first collision-free motion."