Integrating explicit path planning with reactive control of mobile robots using Trulla

Abstract This article describes and investigates a method of interleaving explicit path planning with reactive control. The Trulla all-paths planner computes an a priori set of optimal paths. Minor reactions to obstacles and terrain changes serve to switch the robot from the precomputed path to a new precomputed path, eliminating subgoal obsession. Major deviations suggest that the a priori map is significantly wrong; explicit replanning should be triggered to ensure continued progress of the robot. The dot product is used as the intrinsic measure of a major deviation. This methodology is particularly well-suited for computationally bound robots such as planetary rovers and robots operating in indoor environments with a large number of minor unmodeled obstacles. The article describes the Trulla and dot product algorithms, and reports on experimental data collected from a mobile robot under representative scenarios. The method is compared to continuous and fixed frequency replanning under differing environments and robot velocities. The results show that the deferred replanning with the Trulla/dot product methodology produced actual paths similar to more frequent replanning in distance and time but with up to 100 times less computation. The reduced computation led to a 8.75% increase in distance traveled and 24% increase in travel time. In the presence of faulty sensor data, Trulla outperformed the other methods which radically changed the path back and forth due to spurious sensor readings.