Cost based planning with RRT in outdoor environments
ABSTRACT The Rapidly Exploring Random Tree (RRT) algorithm can be applied to the robotic path planning problem and performs well in challenging, dynamic domains. Traditional RRT methods use a binary cost function and they select portions of the tree for expansion based on the Euclidean distance to the target. However, in outdoor navigation, the relative cost of terrain can also provide useful input to a planning algorithm that traditional RRT methods cannot take advantage of. We present the Metric Adaptive RRT (MA-RRT), which integrates planning and fast execution for generating paths over a cost map. The MA-RRT algorithm considers underlying cost of a path when calculating the distance function for tree expansion. A heuristic value is also used for determining distance from a point to the target and an adaptive mechanism is employed for adjusting the heuristic on-line. We have implemented our approach in offline simulations and in outdoor robot experiments, and show that the MA-RRT algorithm can improve upon the quality of the path returned when cost is considered. The trade off between cost consideration and runtime performance is also presented.
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ABSTRACT: Mapping, real-time localization, and path planning are prerequisites for autonomous robot navigation. These functions also facilitate situation awareness of remote operators. In this paper, we propose methods for e�cient 3D mapping and real-time 6D pose tracking of autonomous robots using a continuously rotating 2D laser scanner. We have developed our approach in the context of the DLR SpaceBot Cup robotics challenge. Multi-resolution surfel representations allow for compact maps and e�cient Registration of local maps. Real-time pose tracking is performed by a particle �lter observing individual laser scan lines. Terrain drivability is assessed within a global environment map and used for planning feasible paths. Our approach is evaluated using challenging real environments.German Journal on Artificial Intelligence. 04/2014; 28(2):93-99.
Conference Paper: A cost-aware path planning algorithm for mobile robots[Show abstract] [Hide abstract]
ABSTRACT: In this paper, we propose a cost-aware path planning algorithm for mobile robots. As a robot moves from one location to another, the robot is penalized by the cost at its current location. The overall cost of the robot is determined by the trajectory of the robot over the cost map. The goal of the proposed cost-aware path planning algorithm is to find the trajectory with the minimal cost. The cost map of a field can represent environmental parameters, such as temperature, humidity, chemical concentration, wireless signal strength, and stealthiness. For example, if the cost map represents packet drop rates at different locations, the minimum cost path between two locations is the path with the best possible communication, which is desirable when a robot operates under the environment with weak wireless signals. The proposed cost-aware path planning algorithm extends the rapidly-exploring random tree (RRT) algorithm by applying the cross entropy (CE) method for extending motion segments. We show that the proposed algorithm finds a path which is close to the near-optimal cost path and gives an outstanding performance compared to RRT and CE-based path planning methods through extensive simulation.Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on; 01/2012
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ABSTRACT: Robust robot motion planning in dynamic environments re-quires that actions be selected under real-time constraints. Existing heuristic search methods that can plan high-speed motions do not guarantee real-time performance in dynamic environments. Existing heuristic search methods for real-time planning in dynamic environments fail in the high-dimensional state space required to plan high-speed actions. In this paper, we present extensions to a leading planner for high-dimensional spaces, R*, that allow it to guarantee real-time performance, and extensions to a leading real-time plan-ner, LSS-LRTA*, that allow it to succeed in dynamic motion planning. In an extensive empirical comparison, we show that the new methods are superior to the originals, providing new state-of-the-art search performance on this challenging problem.