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.
"In , the heuristically-guided RRT (hRRT) biases the search using a quality measure based on the integral of the cost along the path from the root node and an estimation of the optimal cost to the goal. Such an approach, inspired from graph search techniques, can also be found in the context of real-time applications ,  and statistical learning of feasible paths . However, with these techniques, the estimated cost to goal is heuristic and tends to bias the search straight toward the goal at the expense of lower-quality solution paths. "
[Show abstract][Hide abstract] ABSTRACT: This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states. The planner is analyzed and shown to compute low-cost solutions with respect to a path-quality criterion based on the notion of mechanical work. A large set of experimental results is provided to demonstrate the effectiveness of the method. Current limitations and possible extensions are also discussed.
"We compared our approach (Sensor Estimated Graph planner) with the basic RRT  and MA-RRT  to determine the relationship between the final path cost and the distance traveled by the robot using each method. We define the final path cost to be the cost of the path calculated on each model (grid maps for RRT and MA-RRT, and graphs for our approach) after finishing a run. "
[Show abstract][Hide abstract] ABSTRACT: One of the common applications for outdoor robots is to follow a path in large scale unknown environments. This task is challenging due to the intensive memory requirements to represent the map, uncertainties in the location estimate of the robot and unknown terrain type and obstacles on the way to the goal. We develop a novel graph-based path planner that is based on only local perceptual information to plan a path in such environments. In order to extend the capabilities of the graph representation, we introduce exploration bias, which is a node attribute that can implicitly encode obstacle features at immediate surrounding of a node in the graph, the uncertainty of the planner about a node location and also the frequency of visiting a location. Through simulation experiments, we demonstrate that the resulting path cost and distance that the robot traverses to reach the goal location is not significantly different from those of the previous approaches.
Robotics and Automation, 2009. ICRA '09. IEEE International Conference on; 06/2009
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