Motion Planning
Motion planning focuses on generating safe and efficient trajectories for robots and autonomous systems to navigate complex environments and achieve specified goals. Current research emphasizes improving the efficiency of sampling-based methods through techniques like message-passing Monte Carlo and leveraging vision-language models and reinforcement learning for higher-level task planning and decision-making in dynamic scenarios. These advancements are crucial for enabling robots to perform increasingly complex tasks in real-world settings, impacting fields such as robotics, autonomous driving, and multi-agent systems.
434papers
Papers - Page 16
June 15, 2023
June 13, 2023
Mobility Strategy of Multi-Limbed Climbing Robots for Asteroid Exploration
Warley F. R. Ribeiro, Kentaro Uno, Masazumi Imai, Koki Murase, Barış Can Yalçın, Matteo El Hariry, Miguel A. Olivares-Mendez, Kazuya YoshidaMulti-Robot Motion Planning: A Learning-Based Artificial Potential Field Solution
Dengyu Zhang, Guobin Zhu, Qingrui Zhang
June 8, 2023
Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios
Sajjad Mozaffari, Mreza Alipour Sormoli, Konstantinos Koufos, Graham Lee, Mehrdad DianatiMotion Planning for Aerial Pick-and-Place based on Geometric Feasibility Constraints
Huazi Cao, Jiahao Shen, Cunjia Liu, Bo Zhu, Shiyu Zhao
June 1, 2023
May 17, 2023