Navigation & Mobile Manipulation in Challenging and Cluttered Natural Environments
RSS 2024 Half-day Workshop, July 19th
ME Lecture Hall A - Leonardo da Vinci
Thank you very much for the speakers and attendees!
About Workshop
Humans are all connected worldwide – We share air, water, and food from our planet. We have responsibilities to monitor and manage our valuable assets; forests, mountains, and crops therein for our future generations. However, current efforts require intensive human labor; and heavy machines come with a dramatic environmental impact, not only regarding carbon footprints, but also soil compaction and noise disturbing ecosystems. Small robots with some degree of autonomy therefore hold enormous promise. However, much of current autonomous robot research and development focuses on their use inside more structured and controlled built environments. The objective of this workshop is to share visions and recent research progress on enabling robot navigation, as well as physical interaction through manipulation in natural outdoors environments, addressing the vast challenges in perception of completely open-ended, sometimes dynamic, and ever-changing surroundings, as well as navigation and interaction therein.
Topics of Interest
Navigation of aerial and ground robots in outdoor environments
Large-scale and long-term robot state estimation and navigation
Perception systems in complex and dynamic environments
(Dense) mapping in cluttered environments
Integrated planning and learning for outdoor robots
Mobile manipulation for harvesting
Applications to environmental monitoring, forestry, precision agriculture
Program
13:30 ~ 13:40
Introduction
13:40 ~ 14:00
Prof. Fei Gao & Tianyue Wu
Swarm of Micro Robots Flying Out of Laboratory
14:00 ~ 14:20
Dr. Peyman Moghadam
Long-Term Robot Learning in Natural Environments:
Challenges and Opportunities (remote)
14:20 ~ 14:40
Prof. Fu Zhang
UAV navigation and mapping using LiDAR sensors (remote)
14:40 ~ 15:00
Prof. Hyun Myung
Autonomous robot navigation in rough terrains using spatial AI