The proposed aims to develop new adaptive planning algorithms for robotic information gathering in unstructured environments.
We plan to leverage on combination of active sensing, planning, and learning techniques to design a unifying adaptive autonomous data collection planning framework to deal with motion and sensor uncertainties in a dynamic and partially known environment.
We propose to generalize the autonomous data collection planning to improve performance of the data collection mission by adaptive re-planning based on new information gathered during the mission.
In particular we aim to propose and design:
1) specific autonomous data collection planning algorithms for simultaneous determination of sensing locations and trajectory generation
2) adaptive planning algorithm for on-line refinement of traversability cost in dynamic rough terrains for a hexapod walking robot
3) to establish complex analysis and empirical evaluation of the developed solutions.
Involved: Jan Faigl