Motion Planning Networks
By Ahmed Hussain Qureshi, Mayur Joseph Bency, Michael C. Yip. IEEE International Conference on Robotics and Automation, 2018
294 citations recorded by the source corpus.
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue, we present Motion Planning Networks (MPNet), a neural network-based novel planning algorithm. The proposed method encodes the given workspaces directly from a point cloud measurement and generates the end-to-end collision-free paths for the given start and goal configurations. We evaluate MPNet on various 2D and 3D environments including the planning of a 7 DOF Baxter robot manipulator. The results show that MPNet is not only consistently computationally efficient in all environments but also generalizes to completely unseen environments. The results also show that the computation time of MPNet consistently remains less than 1 second in all presented experiments, which is significantly lower than existing state-of-the-art motion planning algorithms.
Subfields: Automotive Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/0c144cd2482334b4629addd7899d026feff2f08f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Roberto Mart'in-Mart'in
- Fei Xia
- Silvio Savarese
- Siddhartha S. Srinivasa
- Chengshu Li
- Corey Lynch
- Jingxi Xu
- Shuran Song
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Silvio Savarese
- Jitendra Malik
- Li Fei-Fei
- Trevor Darrell
- Xiaogang Wang
- Dacheng Tao
- Andrew Zisserman
Citations in the corpus
- Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges (2020, methodology): “The 7-DOF manipulator dataset was generated by randomly perturbing objects from an initial cluttered environment [38].”
- Learning a Decentralized Multi-arm Motion Planner (2020, background): “Some of these works have improved the speed of SMPs by biasing the sampling distribution of new nodes [14, 15, 16], directly predicting the next node [17], utilizing past experience with an extra lookup table module [18], or reducing the search space through discretization [5, 6, 19].”
- Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning (2020, background): “This also allows the embedding of policies in classical motion planning structures [23, 24, 25, 26, 27, 28, 29, 30].”
- ReLMoGen: Integrating Motion Generation in Reinforcement Learning for Mobile Manipulation (2021, background): “Researchers have proposed learning-based solutions that map partial observations to waypoints [22, 23, 24, 25] or trajectories [26], thus bypassing the trajectory searching problem.”
- Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion Planners (2019, methodology): “In our initial work in motion planning with neural networks [8], [9], we highlighted that merging both fields holds a great potential to build motion planning methods with all key features of an ideal planner ranging from theoretical guarantees to computational efficiency.”
- ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation (2020, background): “Researchers have proposed learning-based solutions that map partial observations to either waypoints [22, 23, 24, 25] or whole trajectories [26], thus bypassing the trajectory searching problem.”