Our researcher Jan Faigl gave a talk at the 2nd Workshop on Informative Path Planning and Adaptive Sampling (WIPPAS 2019) organized in conjunction with Robotics: Science and Systems 2019 (RSS 2019) in Freiburg, Germany. His lecture was about data collection planning with curvature-constrained vehicle, which is one of the problem in robotic information gathering.
Abstract: Having a set of locations where sensor measurements can be retrieved, the problem of finding a cost-efficient path to collect data from the locations can be addressed as one of the existing variants of the routing problems such the Traveling Salesman Problem (TSP) or Orienteering Problem (OP). However, a robotic system might be constrained by its motion capabilities, and therefore, there is a fundamental requirement on the feasibility of the found solutions. In this talk, I will present an overview of existing approaches for curvature-constrained systems that can be modeled using Dubins vehicle together with solution quality estimation based on tight lower bounds for Dubins TSP. Besides, a generalization of the data collections planning in 3D, especially suitable for multi-rotor vehicles, will be overviewed. Moreover, recent results on a combination of the routing with motion planning will be introduced as the Physical Orienteering Problem (POP) that provides a general way how to address data collection planning with vehicles for which feasible trajectories can be found by randomized sampling-based motion planning methods.
The main goal of the workshop is to discuss and share ideas related to informative path planning and adaptive sampling. This is a topic that spans all robotic domains and we want to bring together researchers from all fields—marine, ground and aerial robotics, as well as the multi-agent and learning communities—who might otherwise not be aware of the valuable techniques being developed in each of these domains, and the correspondence between their research. This workshop will look at the various aspects of informative path planning, including, but not limited to, its theoretical foundations, active sampling, spatio-temporal variability, multi-robot planning, and its application to real-world problems.