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Title: LEAP-O: Learning to Predict Dynamic Obstacles for Safe Trajectory Planning
Trajectory planning plays a crucial role in autonomous driving and navigation by enabling robots to generate safe paths while minimizing travel costs and avoiding collisions. This paper addresses the issue of predicting dynamic obstacles for safe trajectory planning when prior information is unavailable and detection range is limited. We propose a learning framework using Gaussian Processes (GP) for motion prediction and uncertainty estimation, further enhanced by Recurrent Neural Networks (RNN) for more accurate predictions. In addition, we develop a receding horizon planning method, formulated as a stochastic optimization problem, to ensure safe, collision-free paths with confidence probabilities. Together, these contributions provide a robust framework for adaptive and safe trajectory generation in dynamic environments. Simulations were performed to demonstrate the effectiveness of the proposed strategy, where our approach (combining GP and RNN) outperformed a baseline method that utilized only GP.  more » « less
Award ID(s):
2514584
PAR ID:
10670472
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
IEEE
Date Published:
Format(s):
Medium: X
Location:
New Orleans, Louisiana
Sponsoring Org:
National Science Foundation
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