Rao-Blackwellized particle filter (RBPF) has been to the fore as one of methods to solve simultaneous localization and mapping (SLAM) problem, i.e. RBPF-SLAM. The RBPF-SLAM, however, has been suffering from the particle depletion problem and the convergence problem caused by the improper posterior density and brutal rejection and replication of particles during resampling. We present a new technique to overcome those problems in RBPF-SLAM by keeping the particle diversity using particle formation maintenance (PFM). The triangular mesh structure is adaptively generated as a one form of PFM and it completely replaces the resampling part in RBPF-SLAM. Its considerable improvements regarding robot pose and features were shown in simulation by comparing conventional methods, i.e. FastSLAM 2.0 and PSO based FastSLAM.

This paper presents cooperative object transportation using connected robots. Previous studies of object transportation mainly used pushing or grasping methods to manipulate objects. A pushing method, however, cannot control the object motion precisely and a grasping method requires complicated gripping action before the transportation. Therefore, we suggest a new object transportation method which uses a rope. The method consists of three phases according to the behavior: the approaching, enclosing and transportation phases. Robots and an object have different formation controllers in accordance with their phases. Real experiments are presented to prove the validity of the proposed method.