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Bo Cui

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4

IROS Conference 2024 Conference Paper

RT-RRT: Reverse Tree Guided Real-Time Path Planning/Replanning in Unpredictable Dynamic Environments

  • Bo Cui
  • Rongxin Cui
  • Weisheng Yan
  • Yongkang Wang
  • Shi Zhang

Path planning in unpredictable dynamic environments remains a challenging problem due to the unpredictable appearance, disappearance, and movement of dynamic obstacles during navigation. To address this problem, we propose a reverse tree guided rapid exploration random tree (RTRRT) algorithm that can efficiently perform navigation tasks in dynamic environments. The method first constructs a reverse tree rooted as goal state to search for an initial path. If a collision occurs on the path, The RT-RRT constructs a forward tree rooted as the current robot state in the same configuration space, until it connects with the reverse tree to find a new path. Furthermore, The RT-RRT improves the tree construction method and designs a path optimization strategy to reduce the path cost. The method is validated in different scenarios and has excellent navigation capabilities in unpredictable dynamic environments. In the same scenarios, the RT-RRT algorithm improves the success rate by 16. 7%, reduces the path length by 20. 54% and reduces the travel time by 10X compared to the RRT X algorithm with the same number of samples.

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AAAI Conference 2021 Conference Paper

DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning

  • Bo Cui
  • Guyue Hu
  • Shan Yu

An important challenge for neural networks is to learn incrementally, i. e. , learn new classes without catastrophic forgetting. To overcome this problem, generative replay technique has been suggested, which can generate samples belonging to learned classes while learning new ones. However, such generative models usually suffer from increased distribution mismatch between the generated and original samples along the learning process. In this work, we propose DeepCollaboration (D-Collab), a collaborative framework of deep generative and discriminative models to solve this problem effectively. We develop a discriminative learning model to incrementally update the latent feature space for continual classification. At the same time, a generative model is introduced to achieve conditional generation using the latent feature distribution produced by the discriminative model. Importantly, the generative and discriminative models are connected through bidirectional training to enforce cycle-consistency of mappings between feature and image domains. Furthermore, a domain alignment module is used to eliminate the divergence between the feature distributions of generated images and real ones. This module together with the discriminative model can perform effective sample mining to facilitate incremental learning. Extensive experiments on several visual recognition datasets show that our system can achieve stateof-the-art performance.

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