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Found 5 papers, 1 papers with code
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
Our method combines the strength of CBF for real-time collision-avoidance control and RRT for long-horizon motion planning, by using CBF-induced neural controller (CBF-INC) to generate control signals that steer the system towards sampled configurations by RRT.
Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance
There are two major challenges for scaling up robot navigation around dynamic obstacles: the complex interaction dynamics of the obstacles can be hard to model analytically, and the complexity of planning and control grows exponentially in the number of obstacles.
Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality
Though it is complete and optimal, it does not scale well.
Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks
We propose new learning-based methods for reducing collision checking to accelerate motion planning by training graph neural networks (GNNs) that perform path exploration and path smoothing.
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments.
