Cooperative Motion Planning for Non-Holonomic Agents with Value Iteration Networks
This work addresses cooperative planning challenges for robots with non-holonomic constraints, representing an incremental extension of existing VIN methods.
The paper tackled cooperative motion planning for non-holonomic agents by extending Value Iteration Networks (VINs) to interconnect multiple networks, enabling policies generated via iterative gradient descent to resolve such problems in simulation.
Cooperative motion planning is still a challenging task for robots. Recently, Value Iteration Networks (VINs) were proposed to model motion planning tasks as Neural Networks. In this work, we extend VINs to solve cooperative planning tasks under non-holonomic constraints. For this, we interconnect multiple VINs to pay respect to each other's outputs. Policies for cooperation are generated via iterative gradient descend. Validation in simulation shows that the resulting networks can resolve non-holonomic motion planning problems that require cooperation.