Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Shortest paths in the plane with convex polygonal obstacles
Information Processing Letters
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
A Fuzzy Controller for Autonomous Negotiation of Stairs by a Mobile Robot with Adjustable Tracks
RoboCup 2007: Robot Soccer World Cup XI
Journal of Field Robotics - Vehicle–Terrain Interaction for Mobile Robots
Sampling-based path planning on configuration-space costmaps
IEEE Transactions on Robotics
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Rescue robots are platforms designed to operate in challenging and uneven surfaces. These robots are often equipped with manipulator arms and varying traction arrangements. As such, it is possible to reconfigure the kinematic of robot in order to reduce potential instabilities, such as those leading to vehicle tip-over. This paper proposes a methodology to plan feasible paths through uneven topographies by planning stable paths that account for the safe interaction between vehicle and terrain. The proposed technique, based on a gradient stability criterion, is validated with two of the best known path search strategies in 3D lattices, i.e. the A* and the Rapidly-Exploring Random Trees. Using real terrain data, simulation results obtained with the model of a real rescue robot demonstrate significant improvements in terms of paths that are able to automatically avoid regions of potential instabilities, to concentrate on those where the freedom of exploiting posture adaptation permits generation of optimally safe paths.