Paper Info
Title
Autonomous Navigation for Quadrupedal Robots with Optimized Jumping through Constrained Obstacles.
Abstract
Quadrupeds are strong candidates for navigating challenging environments because of their agile and dynamic designs. This paper presents a methodology that extends the range of exploration for quadrupedal robots by creating an end-to-end navigation framework that exploits walking and jumping modes. To obtain a dynamic jumping maneuver while avoiding obstacles, dynamically-feasible trajectories are optimized offline through collocation-based optimization where safety constraints are imposed. Such optimization schematic allows the robot to jump through window-shaped obstacles by considering both obstacles in the air and on the ground. The resulted jumping mode is utilized in an autonomous navigation pipeline that leverages a search-based global planner and a local planner to enable the robot to reach the goal location by walking. A state machine together with a decision making strategy allows the system to switch behaviors between walking around obstacles or jumping through them. The proposed framework is experimentally deployed and validated on a quadrupedal robot, a Mini Cheetah, to enable the robot to autonomously navigate through an environment while avoiding obstacles and jumping over a maximum height of 13 cm to pass through a window-shaped opening in order to reach its goal.
Year
DOI
Venue
2021
10.1109/CASE49439.2021.9551524
CASE
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
11
Name
Order
Citations
PageRank
Scott Gilroy100.34
Derek Lau200.34
Lizhi Yang301.01
Ed Izaguirre400.34
Kristen Biermayer500.34
Anxing Xiao600.68
Mengti Sun700.34
Ayush Agrawal853.90
Jun Zeng901.01
Zhongyu Li1000.68
Koushil Sreenath1135833.41