Path Planning in Dynamic Environments using Generative RNNs and Monte
Carlo Tree Search
- URL: http://arxiv.org/abs/2001.11597v1
- Date: Thu, 30 Jan 2020 22:46:37 GMT
- Title: Path Planning in Dynamic Environments using Generative RNNs and Monte
Carlo Tree Search
- Authors: Stuart Eiffert, He Kong, Navid Pirmarzdashti and Salah Sukkarieh
- Abstract summary: State of the art methods for robotic path planning in dynamic environments, such as crowds or traffic, rely on hand crafted motion models for agents.
This paper proposes an integrated path planning framework using generative Recurrent Neural Networks within a Monte Carlo Tree Search (MCTS)
We show that the proposed framework can considerably improve motion prediction accuracy during interactions.
- Score: 11.412720572948086
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: State of the art methods for robotic path planning in dynamic environments,
such as crowds or traffic, rely on hand crafted motion models for agents. These
models often do not reflect interactions of agents in real world scenarios. To
overcome this limitation, this paper proposes an integrated path planning
framework using generative Recurrent Neural Networks within a Monte Carlo Tree
Search (MCTS). This approach uses a learnt model of social response to predict
crowd dynamics during planning across the action space. This extends our recent
work using generative RNNs to learn the relationship between planned robotic
actions and the likely response of a crowd. We show that the proposed framework
can considerably improve motion prediction accuracy during interactions,
allowing more effective path planning. The performance of our method is
compared in simulation with existing methods for collision avoidance in a crowd
of pedestrians, demonstrating the ability to control future states of nearby
individuals. We also conduct preliminary real world tests to validate the
effectiveness of our method.
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