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Navigating dynamic environments requires the robot to generate collision-free trajectories and actively avoid moving obstacles. Most previous works designed path planning algorithms based on one single map representation. Although they have shown success in static environments, due to the limitation of map representation, those methods cannot reliably handle static and dynamic obstacles simultaneously. To address the problem, We proposes a gradient-based B-spline trajectory optimization algorithm utilizing the robot’s onboard vision.
A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance
We Developed methods for stabilizing large scale power systems based on Input-to-State Stability (ISS) Lyapunov-based neural certificate, by treating a large system as an interconnection of smaller subsystems. Each ISS Lyapunov function of subsystem could be collected to prove the global stability of power system.
We Developed deep reinforcement learning (DRL) based social navigation approach for multiple intelligent robots to safely move in pedestrian-rich environments via cooperative perception.
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
Published in RA-L, 2023
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). https://arxiv.org/abs/2303.00132
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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