Tag Archives: Graphs

Several strategies for exploring unknown environments based on graphs extracted from Voronoi diagrams

E. G. Tsardoulias, A. Iliakopoulou, A. Kargakos, L. Petrou, Cost-Based Target Selection Techniques Towards Full Space Exploration and Coverage for USAR applications in a Priori Unknown Environments, J Intell Robot Syst (2017) 87:313–340, DOI: 10.1007/s10846-016-0434-0.

Full coverage and exploration of an environment is essential in robot rescue operations where victim identification is required. Three methods of target selection towards full exploration and coverage of an unknown space oriented for Urban Search and Rescue (USAR) applications have been developed. These are the Selection of the closest topological node, the Selection of the minimum cost topological node and the Selection of the minimum cost sub-graph. All methods employ a topological graph extracted from the Generalized Voronoi Diagram (GVD), in order to select the next best target during exploration. The first method utilizes a distance metric for determining the next best target whereas the Selection of the minimum cost topological node method assigns four different weights on the graph’s nodes, based on certain environmental attributes. The Selection of the minimum cost sub-graph uses a similar technique, but instead of single nodes, sets of graph nodes are examined. In addition, a modification of A* algorithm for biased path creation towards uncovered areas, aiming at a faster spatial coverage, is introduced. The proposed methods’ performance is verified by experiments conducted in two heterogeneous simulated environments. Finally, the results are compared with two common exploration methods.

Learning concepts from graphs in robotics, through first-order logic and discovery of subgraphs, forming arbitrary hierarchies

Ana C. Tenorio-González, Eduardo F. Morales, Automatic discovery of relational concepts by an incremental graph-based representation, Robotics and Autonomous Systems, Volume 83, 2016, Pages 1-14, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.06.012.

Automatic discovery of concepts has been an elusive area in machine learning. In this paper, we describe a system, called ADC, that automatically discovers concepts in a robotics domain, performing predicate invention. Unlike traditional approaches of concept discovery, our approach automatically finds and collects instances of potential relational concepts. An agent, using ADC, creates an incremental graph-based representation with the information it gathers while exploring its environment, from which common sub-graphs are identified. The subgraphs discovered are instances of potential relational concepts which are induced with Inductive Logic Programming and predicate invention. Several concepts can be induced concurrently and the learned concepts can form arbitrarily hierarchies. The approach was tested for learning concepts of polygons, furniture, and floors of buildings with a simulated robot and compared with concepts suggested by users.