Gross motion planning—a survey
ACM Computing Surveys (CSUR)
Robot Motion Planning
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy ofSpatial Representations
A Robot Exploration and Mapping Strategy Based on a Semantic Hierarchy ofSpatial Representations
Topologically-directed navigation
Robotica
Complexity of the mover's problem and generalizations
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
A mobius automation: an application of artificial intelligence techniques
IJCAI'69 Proceedings of the 1st international joint conference on Artificial intelligence
Evaluation of navigation of an autonomous mobile robot
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
Hybrid robot control and SLAM for persistent navigation and mapping
Robotics and Autonomous Systems
Proceedings of the Workshop on Performance Metrics for Intelligent Systems
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The evaluation of the performance of robot motion methods and systems is still an open challenge, although substantial progress has been made in the field over the years. On the one hand, these techniques cannot be evaluated off-line, on the other hand, they are deeply influenced by the task, the environment and the specific representation chosen for it. In this paper we concentrate on "pure-motion tasks": tasks that require to move the robot from one configuration to another, either being an independent sub-task of a more complex plan or representing a goal by itself. After characterizing the goals and the tasks, we describe the commonly-used problem decomposition and different kinds of modeling that can be used, from accurate metric maps to minimalistic representations. The contribution of this paper is an evaluation framework that we adopt in a set of experiments showing how the performance of the motion system can be affected by the use of different kinds of environment representations.