PHA*: Finding the Shortest Path with A* in An Unknown Physical Environment

We address the problem of finding the shortest path between two points in an unknown real physical environment, where a traveling agent must move around in the environment to explore unknown territory. We introduce the Physical-A* algorithm (PHA*) for solving this problem. PHA* expands all the mandatory nodes that A* would expand and returns the shortest path between the two points. However, due to the physical nature of the problem, the complexity of the algorithm is measured by the traveling effort of the moving agent and not by the number of generated nodes, as in standard A*. PHA* is presented as a two-level algorithm, such that its high level, A*, chooses the next node to be expanded and its low level directs the agent to that node in order to explore it. We present a number of variations for both the high-level and low-level procedures and evaluate their performance theoretically and experimentally. We show that the travel cost of our best variation is fairly close to the optimal travel cost, assuming that the mandatory nodes of A* are known in advance. We then generalize our algorithm to the multi-agent case, where a number of cooperative agents are designed to solve the problem. Specifically, we provide an experimental implementation for such a system. It should be noted that the problem addressed here is not a navigation problem, but rather a problem of finding the shortest path between two points for future usage

Integrated motion planning and model learning for mobile robots with application to marine vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 269-275).Robust motion planning algorithms for mobile robots consider stochasticity in the dynamic model of the vehicle and the environment. A practical robust planning approach balances the duration of the motion plan with the probability of colliding with obstacles. This thesis develops fast analytic algorithms for predicting the collision probability due to model uncertainty and random disturbances in the environment for a planar holonomic vehicle such as a marine surface vessel. These predictions lead to a robust motion planning algorithm that nds the optimal motion plan quickly and efficiently. By incorporating model learning into the predictions, the integrated algorithm exhibits emergent active learning strategies to autonomously acquire the model data needed to safely and eectively complete the mission. The motion planner constructs plans through a known environment by concatenating maneuvers based upon speed controller setpoints. A model-based feedforward/ feedback controller is used to track the resulting reference trajectory, and the model parameters are learned online with a least squares regression algorithm. The path-following performance of the vehicle depends on the effects of unknown environmental disturbances and modeling error. The convergence rate of the parameter estimates depends on the motion plan, as different plans excite different modes of the system.(cont.) By predicting how the collision probability is affected by the parameter covariance evolution, the motion planner automatically incorporates active learning strategies into the motion plans. In particular, the vehicle will practice maneuvers in the open regions of the configuration space before using them in the constrained regions to ensure that the collision risk due to modeling error is low. High-level feedback across missions allows the system to recognize configuration changes and quickly learn new model parameters as necessary. Simulations and experimental results using an autonomous marine surface vessel are presented.by Matthew Greytak.Ph.D