Avoiding blind leading the blind

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.

On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps

“…Even if the person is falsely identified as a new obstacle and the map is updated, it has no adverse effects in the proposed method, as uncertainty is integrated in the confidence decay mechanism. Any wrong map update corresponding to dynamic obstacles has high probability of larger positional uncertainty corresponding to the dynamic obstacle and therefore a quicker decay given by Equation ( 6), (7) and (9). On the other hand, for static new obstacles in the map, the underlying SLAM (Algorithm 2) algorithm estimates smaller positional uncertainty and therefore a larger decay time, ensuring its permanence in the map.…”

“…In-fact, multi-robot sport activities like Robo-soccer [7,8] heavily relies on meaningful information sharing between robots to achieve a common goal. Virtual pheromones have been proposed to be used for coordinating master-slave robots in References [9,10]. Path planning of multiple robots using information from external security cameras is proposed in Reference [11].…”

Transient Virtual Obstacles for Safe Robot Navigation in Indoor Environments

“…It is assumed that robots have a Simultaneous Localization and Mapping (SLAM) module to build a map and localize themselves in it [5,6,7]. The case of multi-robot navigation has been extensively discussed in [8,9,10,11,12]. Some researchers have focussed on realizing a robot that acquires logical recognition of space [13].…”

Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges

“…The localized position of the robot is the start location, and the goal location is specified for the robot for it to navigate towards the goal using any of the state-of-the-art path planning algorithms. SLAM is considered to be a core module for autonomous mobile robots [ 7 ] because it is often a prerequisite to path planning, navigation, and manipulation for single and multi-robot systems [ 8 , 9 , 10 , 11 ].…”