Learning Configuration Space Belief Model from Collision Checks for Motion Planning
This work addresses a key challenge in robotics motion planning for reducing computational costs, though it appears incremental as it builds on existing probabilistic modeling approaches.
The paper tackles the computational bottleneck of collision detection in high-dimensional motion planning by developing a belief model of the configuration space using collision test results, with their proposed topological method outperforming kNN methods in accuracy and efficiency.
For motion planning in high dimensional configuration spaces, a significant computational bottleneck is collision detection. Our aim is to reduce the expected number of collision checks by creating a belief model of the configuration space using results from collision tests. We assume the robot's configuration space to be a continuous ambient space whereby neighbouring points tend to share the same collision state. This enables us to formulate a probabilistic model that assigns to unevaluated configurations a belief estimate of being collision-free. We have presented a detailed comparative analysis of various kNN methods and distance metrics used to evaluate C-space belief. We have also proposed a weighting matrix in C-space to improve the performance of kNN methods. Moreover, we have proposed a topological method that exploits the higher order structure of the C-space to generate a belief model. Our results indicate that our proposed topological method outperforms kNN methods by achieving higher model accuracy while being computationally efficient.