Autonomous Robot
Autonomous robots are being developed to perform complex tasks in diverse and unpredictable environments, focusing on robust navigation, adaptive behavior, and reliable operation. Current research emphasizes improving perception through advanced computer vision techniques (e.g., keypoint detection, neural radiance fields) and developing efficient planning algorithms (e.g., deep reinforcement learning, hierarchical decision networks, belief space search) that incorporate uncertainty and handle dynamic situations. These advancements are significant for various applications, including exploration, manufacturing, agriculture, and search and rescue, promising increased efficiency and safety in challenging real-world scenarios.
Papers
Monitoring ROS2: from Requirements to Autonomous Robots
Ivan Perez, Anastasia Mavridou, Tom Pressburger, Alexander Will, Patrick J. Martin
OA-Bug: An Olfactory-Auditory Augmented Bug Algorithm for Swarm Robots in a Denied Environment
Siqi Tan, Xiaoya Zhang, Jingyao Li, Ruitao Jing, Mufan Zhao, Yang Liu, Quan Quan
Autonomous social robot navigation in unknown urban environments using semantic segmentation
Sophie Buckeridge, Pamela Carreno-Medrano, Akansel Cosgun, Elizabeth Croft, Wesley P. Chan
Elly: A Real-Time Failure Recovery and Data Collection System for Robotic Manipulation
Elena Galbally, Adrian Piedra, Cynthia Brosque, Oussama Khatib