A Day/Night Leader-Following Method Based on Adaptive Federated Filter for Quadruped Robots

The quadruped robots have superior adaptability to complex terrains, compared with tracked and wheeled robots. Therefore, leader-following can help quadruped robots accomplish long-distance transportation tasks. However, long-term following has to face the change of day and night as well as the presence of interference. To solve this problem, we present a day/night leader-following method for quadruped robots toward robustness and fault-tolerant person following in complex environments. In this approach, we construct an Adaptive Federated Filter algorithm framework, which fuses the visual leader-following method and the LiDAR detection algorithm based on reflective intensity. Moreover, the framework uses the Kalman filter and adaptively adjusts the information sharing factor according to the light condition. In particular, the framework uses fault detection and multisensors information to stably achieve day/night leader-following. The approach is experimentally verified on the quadruped robot SDU-150 (Shandong University, Shandong, China). Extensive experiments reveal that robots can identify leaders stably and effectively indoors and outdoors with illumination variations and unknown interference day and night.

Defining the Number of Mobile Robotic Systems Needed for Reconfiguration of Modular Manufacturing Systems via Simulation

The European vision of the Factory of the Future is based on increasing competition and sustainability by transformation from cost orientation to high-adding value with technical and organisational innovations. One of the expected outcomes is an increase in modularisation, i.e., the reconfigurability of the technical system in manufacturing conditions. Modular manufacturing systems (MMS), will consist of modular platforms (MP) that are capable of rapid rebuilding, and reconfiguration performed by adding or removing a module by Mobile Robotic Systems (MRS). In the conditions of MMS, to make the most efficient use of reconfiguration MRS capacities, it is necessary to know the optimal ratio of these MRS to the number of modular platforms (MP) used in MMS, which does not exist today. This ratio will help industrial companies that are deploying MMS-based solutions to plan the number of MRSs needed to reconfigure deployed systems. As a method of determining this optimal ratio, an experimental approach via simulation was chosen, using data from custom MRS and MP prototypes with testing different layouts of modular platforms with the view of warehouse layout, manufacturing island, manufacturing island power supply, and MRS. Based on the results, it can be determined that the MP-MRS limit ratio is 2:1, where the further increase in MRS has only a minimal impact on the reconfiguration period. With the reduction of MP transferred to one MRS, there is a gradual decrease in the time required for reconfiguration. When the ratio of 1:1 is attained, the time required for reconfiguration lowers, but not as dramatically as in bigger ratios.

DV-LOAM: Direct Visual LiDAR Odometry and Mapping

Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged direct visual odometry module, which consists of a frame-to-frame tracking step, and an improved sliding window based thinning step, is proposed to estimate the accurate pose of the camera while maintaining efficiency. Secondly, every time a keyframe is generated, a dynamic objects considered LiDAR mapping module is utilized to refine the pose of the keyframe to obtain higher positioning accuracy and better robustness. Finally, a Parallel Global and Local Search Loop Closure Detection (PGLS-LCD) module that combines visual Bag of Words (BoW) and LiDAR-Iris feature is applied for place recognition to correct the accumulated drift and maintain a globally consistent map. We conducted a large number of experiments on the public dataset and our mobile robot dataset to verify the effectiveness of each module in our framework. Experimental results show that the proposed algorithm achieves more accurate pose estimation than the state-of-the-art methods.