Elevation moment of inertia: A new feature for Monte Carlo localization in outdoor environment with elevation map

A novel edge gradient algorithm for multiple mobile robots cooperative mapping in unknown environment

This article presented a cooperative mapping technique using a novel edge gradient algorithm for multiple mobile robots. The proposed edge gradient algorithm can be divided into four behaviors such as adjusting the movement direction, evaluating the safety of motion behavior, following behavior, and obstacle information exchange, which can effectively prevent multiple mobile robots falling into concave obstacle areas. Meanwhile, a visual field factor is constructed based on biological principles so that the mobile robots can have a larger field of view when moving away from obstacles. Also, the visual field factor will be narrowed due to the obstruction of the obstacle when approaching an obstacle and the obtained map-building data are more accurate. Finally, three sets of simulation and experimental results demonstrate the performance superiority of the presented algorithm.

Landmark-Based Homing Navigation Using Omnidirectional Depth Information

A number of landmark-based navigation algorithms have been studied using feature extraction over the visual information. In this paper, we apply the distance information of the surrounding environment in a landmark navigation model. We mount a depth sensor on a mobile robot, in order to obtain omnidirectional distance information. The surrounding environment is represented as a circular form of landmark vectors, which forms a snapshot. The depth snapshots at the current position and the target position are compared to determine the homing direction, inspired by the snapshot model. Here, we suggest a holistic view of panoramic depth information for homing navigation where each sample point is taken as a landmark. The results are shown in a vector map of homing vectors. The performance of the suggested method is evaluated based on the angular errors and the homing success rate. Omnidirectional depth information about the surrounding environment can be a promising source of landmark homing navigation. We demonstrate the results that a holistic approach with omnidirectional depth information shows effective homing navigation.

Prototyping of Fully Autonomous Indoor Patrolling Mobile Robots

In this chapter, the design and prototyping of an indoor patrolling mobile robot is presented. This robot employs a modular design strategy by using the ROS (Robot Operating System) software framework, which allows for an agile development and testing process. The primary modules - omni-directional drive system, localization, navigation, and autonomous charging - are described in detail. Special efforts have been put into the design of these modules to make them reliable and robust in order to achieve autonomous patrolling without human intervention. With experimental tests, the authors show that an indoor mobile robot patrolling autonomously in a typical office environment is realizable.