Authors:
Arjun Nagendran
1
;
Remo Pillat
1
and
Robert Richardson
2
Affiliations:
1
University of Central Florida, United States
;
2
University of Leeds, United Kingdom
Keyword(s):
Object Capture, Trajectory Planning, Minimum-Jerk.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Artificial Intelligence
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Real-Time Systems Control
;
Signal Processing, Sensors, Systems Modeling and Control
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
This paper presents a method for capturing a free-moving object in the presence of noise and uncertainty
with respect to its estimated position and velocity. The approach is based on Hermite polynomials and involves
matching the state-space parameters of the object and the end effector at the moment of contact. The
method involves real-time re-planning of the robot trajectory whenever new estimates of the object’s motion
parameters are available. Continuity in position, velocity, and acceleration is preserved independently of the
planning update rate and the resulting trajectories are characterized by low jerk. Compared to other methods
that directly solve for higher-order polynomial coefficients, the proposed algorithm is computationally efficient
and does not require a linear solver. Experimental results confirm the advantages of this method during
real-time interception of a dynamically moving object with continuous velocity estimation and high-frequency
re-planning.