This paper presents a multi-agent behavior to cooperatively rescue a faulty robot using a sound signal. In a robot team, the faulty robot should be immediately recalled since it may seriously obstruct other robots, or collected matters in the faulty robot may be lost. For the rescue mission, we first developed a sound localization method, which estimates the sound source from a faulty robot by using multiple microphone sensors. Next, since a single robot cannot recall the faulty robot, the robots organized a heterogeneous rescue team by themselves with pusher, puller, and supervisor. This self-organized team succeeded in moving the faulty robot to a safe zone without help from any global positioning systems. Finally, our results demonstrate that a faulty robot among multi-agent robots can be immediately rescued with the cooperation of its neighboring robots and interactive communication between the faulty robot and the rescue robots. Experiments are presented to test the validity and practicality of the proposed approach.

This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which showed improved performance without precise controller design. However, DRL-based techniques not only take a long time to learn their policies but also sometimes fail to learn. It is difficult to learn the policy of DRL by random actions only. Therefore, we propose two curricula for the efficient learning of object transportation: region-growing and single- to multi-robot. During the learning process, the region-growing curriculum gradually extended to a region in which an object was initialized. This step-by-step learning raised the success probability of object transportation by restricting the working area. Multiple robots could easily learn a new policy by exploiting the pre-trained policy of a single robot. This single- to multi-robot curriculum can help robots to learn a transporting method with trial and error. Simulation results are presented to verify the proposed techniques.

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SUMMARYThis paper presents an improved Rao-Blackwellized particle filtering framework with consideration of the particle swarm characteristics in FastSLAM, called Relational FastSLAM or R-FastSLAM. The R-FastSLAM seeks to cope with the inherent problems of FastSLAM, i.e., a particle depletion problem and an error accumulation problem in large environments. The R-FastSLAM uses the particle swarm characteristics in calculating the importance weight and maintaining a particle formation. We assign more accurate weights to particles by clustering them using the Expectation-Maximization (EM) algorithm according to an adaptive weight compensation scheme. In addition, particles constitute an adaptive triangular mesh formation to maintain multiple data association hypotheses without any resampling step. Its outstanding accomplishments are verified on simulations and a test using the Victoria Park dataset by comparing the standard FastSLAM 2.0 with the particle swarm optimization based FastSLAM.