Abstract
Navigation and exploration in 3D environments is still a challenging task for autonomous robots that move on the ground. Robots for Search and Rescue missions must deal with unstructured and very complex scenarios. This paper presents a path planning system for navigation and exploration of ground robots in such situations. We use (unordered) point clouds as the main sensory input without building any explicit representation of the environment from them. These 3D points are employed as space samples by an Optimal-RRTplanner (RRT) to compute safe and efficient paths. The use of an objective function for path construction and the natural exploratory behaviour of the RRT planner make it appropriate for the tasks. The approach is evaluated in different simulations showing the viability of autonomous navigation and exploration in complex 3D scenarios.
Highlights
The development of autonomous robots capable of operating in dangerous situations for humans like inspection, surveillance or search and rescue, is a topic of intense investigation in the field of Robotics
Some examples of robotic systems devised for real rescue applications are a multi-flipper controlled platform for collapsed environments [1], the team of teleoperated robots employed in the earthquake in Italy in 2012 [2] or a review of terrestrial robotic systems for nuclear environments, like
The proposed path planning and exploration system has been implemented in C++ under ROS
Summary
The development of autonomous robots capable of operating in dangerous situations for humans like inspection, surveillance or search and rescue, is a topic of intense investigation in the field of Robotics. Some examples of robotic systems devised for real rescue applications are a multi-flipper controlled platform for collapsed environments [1], the team of teleoperated robots employed in the earthquake in Italy in 2012 [2] or a review of terrestrial robotic systems for nuclear environments, like. It is remarkable that most of the real robotic applications are teleoperated, which indicates the complexity of developing effective autonomous behaviour in such environments. Autonomous navigation is a complex function for performing the referred tasks. Within such cluttered and diverse situations, robots must address several issues like localization, perception and representation of the environment, or planning optimal paths according to given criteria. In the case of ground robots, an assessment of the terrain traversability is required [4]
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