Avoiding blind leading the blind: Uncertainty integration in virtual pheromone deposition by robots. (11th November 2016)

• Record Type: Journal Article
• Authors: Ravankar, Abhijeet; Ravankar, Ankit A; Kobayashi, Yukinori; Emaru, Takanori
• Is Part Of: International journal of advanced robotic systems. Volume 13:Number 6(2016)
• Publication Date: 2016-11-11
• Subjects: Multi-robot navigation using pheromone trailing -- pheromone integration in SLAM -- virtual pheromones in mobile robotics; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881416666088
• Languages: English
• ISSNs: 1729-8806

Abstract

Virtual pheromone trailing has successfully been demonstrated for navigation of multiple robots to achieve a collective goal. Many previous works use a pheromone deposition scheme that assumes perfect localization of the robot, in which, robots precisely know their location in the map. Therefore, pheromones are always assumed to be deposited at the desired place. However, it is difficult to achieve perfect localization of the robot due to errors in encoders and sensors attached to the robot and the dynamics of the environment in which the robot operates. In real-world scenarios, there is always some uncertainty associated in estimating the pose (i.e. position and orientation) of the mobile service robot. Failing to model this uncertainty would result in service robots depositing pheromones at wrong places. A leading robot in the multi-robot system might completely fail to localize itself in the environment and be lost. Other robots trailing its pheromones will end up being in entirely wrong areas of the map. This results in a "blind leading the blind" scenario that reduces the efficiency of the multi-robot system. We propose a pheromone deposition algorithm, which models the uncertainty of the robot's pose. We demonstrate, through experiments in both simulated and real environments, that modeling the uncertainty in pheromone deposition is crucial, and that the proposed algorithm can model the uncertainty well.