This paper presents a learning based motion planning method for robotic manipulation, aiming to solve the asymptotically-optimal motion planning problem with nonlinear kinematics in a complex environment. The core of the proposed method is based on a novel neural network model, i.e., graph wasserstein autoencoder (GraphWAE) network, which is used to represent the implicit sampling distributions of the configuration space (C-space) for sampling-based planning algorithms. Through learning the implicit distributions, we can guide the planning process to search or extend in the desired region to reduce the collision checks dramatically for fast and high-quality motion planning. The theoretical analysis and proofs are given to demonstrate the probabilistic completeness and asymptotic optimality of the proposed method. Numerical simulations and experiments are conducted to validate the effectiveness of the proposed method through a series of planning problems from 2D, 6D and 12D robot C-spaces in the challenging scenes. Results indicate that the proposed method can achieve better planning performance than the state-of-the-art planning algorithms. Note to Practitioners - The motivation of this work is to develop a fast and high-quality asymptotically optimal motion planning method for practical applications such as autonomous driving, robotic manipulation and others. Due to the time consumption caused by collision detection, current planning algorithms usually take much time to converge to the optimal motion path especially in the complicated environment. In this paper, we present a neural network model based on GraphWAE to learn the biasing sampling distributions as the sample generation source to further reduce or avoid collision checks of sampling-based planning algorithms. The proposed method is general and can be also deployed in other sampling-based planning algorithms for improving planning performance in different robot applications.
|Number of pages||14|
|Journal||IEEE Transactions on Automation Science and Engineering|
|Early online date||9 Feb 2022|
|Publication status||Published (in print/issue) - 6 Jan 2023|
Bibliographical notePublisher Copyright:
This work was supported in part by the National Natural Science Foundation of China under Grant 61973066, Grant 61803221, and Grant U1813216; in part by the Equipment Pre-Research Foundation under Grant 61403120111; in part by the Major Science and Technology Projects of Liaoning Province under Grant 2021JH1/10400049; in part by the Foundation of Key Laboratory of Equipment Reliability under Grant WD2C20205500306; in part by the Fundamental Research Funds for the Central Universities under Grant N2004022; and in part by the Guangdong Young Talent with Scientific and Technological Innovation under Grant 2019TQ05Z111
© 2004-2012 IEEE.
- Collision avoidance
- collision detection
- graph wasserstein autoencoder
- kinematic constraints
- Motion planning
- Neural networks
- Probabilistic logic
- robotic manipulation.
- System dynamics
- robotic manipulation