In Computational Robotics laboratory (ComRob) we perform fundamental and applied research at the intersection of the artificial intelligence and autonomous robotic systems. We are seeking for unique solutions to address real world challenges to improve quality of life and to understand principles emerging in nature.
We work on demanding problems of planning and control of complex robotic systems in autonomous data collection that require a deployment of new adaptive and learning techniques. Our focus is not limited to build remotely-controlled robots, but we further develop advanced techniques for efficient solution of problems related to environmental monitoring, surveillance, and reconnaissance missions.
- Adaptive Informative Path Planning in Autonomous Data Collection in Dynamic Unstructured Environments, Czech Science Foundation (GA ČR), project No. 15-09600Y (2015-2017)
- Self-Organizing Maps for Multi-Goal Path Planning Tasks, Czech Science Foundation (GA ČR), project No. 13-18316P (2013-2015)
Adaptive Informative Path Planning in Autonomous Data Collection in Dynamic Unstructured Environments
In this project, we aim 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; and 3) to establish complex analysis and empirical evaluation of the developed solutions.
Self-Organizing Maps for Multi-Goal Path Planning Tasks
The project aims to investigate principles of self-organizing maps (SOM) for routing problems in high-dimensional configuration spaces. The addressed problems are from the family of multi-goal path and motion planning problems, where an optimal path or trajectory connecting a given set of goals has to be found. The motivation of the proposed scientific effort is to find a path/trajectory regarding realistic capabilities of mobile robots including their autonomous navigation. Thus, a found path would improve reliability and robustness of the autonomous navigation enabling robotic applications in daily tasks. The project comprises three scientific objectives. The first one deals with finding a representation of high-dimensional configuration space allowing usage of SOM principles for planning in an effective way. The second objective aims to develop a multi-goal motion planning algorithm providing a local planner and estimation of distance metric in the space. Finally, the third objective is to establish the planning framework considering localization uncertainties.