Abstract-In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.
We revisit in this paper the resilience problem of routing traffic in a parallel link network model with a malicious player using a game theoretic framework. Consider that there are two players in the network: the first player wishes to split its traffic so as to minimize its average delay, which the second player, i.e., the malicious player, tries to maximize. The first player has a demand constraint on the total traffic it routes. The second player controls the link capacities: it can decrease by some amount the capacity of each link under a constraint on the sum of capacity degradation. We first show that the average delay function is convex both in traffic and in capacity degradation over the parallel links and thus does not have a saddle point. We identify best responses strategies of each player and compute both the max-min and the minmax values of the game. We are especially interested in the min max strategy as it guarantees the best performance under worst possible link capacity degradation. It thus allows to obtain routing strategies that are resilient and robust. We compare the results of the min-max to those obtained under the max-min strategies. We provide stable algorithms for computing both max-min and min-max strategies as well as for best responses.
We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size. The focus is on producing decisions that can be physically implemented by a robotic loading arm, a laboratory prototype used for testing the concept. The problem considered in this paper is novel in two ways. First, unlike the traditional 3D bin packing problem, we assume that the entire set of objects to be packed is not known a priori. Instead, a fixed number of upcoming objects is visible to the loading system, and they must be loaded in the order of arrival. Second, the goal is not to move objects from one point to another via a feasible path, but to find a location and orientation for each object that maximises the overall packing efficiency of the bin(s). Finally, the learnt model is designed to work with problem instances of arbitrary size without retraining. Simulation results show that the RLbased method outperforms state-of-the-art online bin packing heuristics in terms of empirical competitive ratio and volume efficiency.
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