Efficient collective search by BRT algorithm using fast rising threshold agent

The BRT algorithm is a method for the best-of-n problem that allows a group of distributed robots to find out the most appropriate collective option among many alternatives. Computer experiments show that the time required for finding out the best option is proportional to the number of options. In this paper, we aim to shorten this search time by introducing a few agents whose threshold increases faster than the normal one to achieve higher scalability of the BRT algorithm. The results show that the search time is reduced, and the variance is improved, especially under challenging problems where robots are required to make decisions out of a large number of options.

Genetic Fuzzy Based Scalable System of Distributed Robots for a Collaborative Task

This paper introduces a new genetic fuzzy based paradigm for developing scalable set of decentralized homogenous robots for a collaborative task. In this work, the number of robots in the team can be changed without any additional training. The dynamic problem considered in this work involves multiple stationary robots that are assigned with the goal of bringing a common effector, which is physically connected to each of these robots through cables, to any arbitrary target position within the workspace of the robots. The robots do not communicate with each other. This means that each robot has no explicit knowledge of the actions of the other robots in the team. At any instant, the robots only have information related to the common effector and the target. Genetic Fuzzy System (GFS) framework is used to train controllers for the robots to achieve the common goal. The same GFS model is shared among all robots. This way, we take advantage of the homogeneity of the robots to reduce the training parameters. This also provides the capability to scale to any team size without any additional training. This paper shows the effectiveness of this methodology by testing the system on an extensive set of cases involving teams with different number of robots. Although the robots are stationary, the GFS framework presented in this paper does not put any restriction on the placement of the robots. This paper describes the scalable GFS framework and its applicability across a wide set of cases involving a variety of team sizes and robot locations. We also show results in the case of moving targets.

Robot Ethics 2.0

As a game-changing technology, robotics naturally will create ripple effects through society. Some of them may become tsunamis. So it’s no surprise that “robot ethics”—the study of these effects on ethics, law, and policy—has caught the attention of governments, industry, and the broader society, especially in the past several years. Since our first book on the subject in 2012, a groundswell of concern has emerged, from the Campaign to Stop Killer Robots to the Campaign Against Sex Robots. Among other bizarre events, a robot car has killed its driver, and a kamikaze police robot bomb has killed a sniper. Given these new and evolving worries, we now enter the second generation of the debates—robot ethics 2.0. This edited volume is a one-stop authoritative resource for the latest research in the field, which is often scattered across academic journals, books, media articles, reports, and other channels. Without presuming much familiarity with either robotics or ethics, this book helps to make the discussion more accessible to policymakers and the broader public, as well as academic audiences. Besides featuring new use-cases for robots and their challenges—not just robot cars, but also space robots, AI, and the internet of things (as massively distributed robots)—we also feature one of the most diverse group of researchers on the subject for truly global perspectives.

Distributed Robots Path/Tasks Planning on Fetch Scheduling

A highly concurrent task-planner for distributed multi-robot systems in dynamical industrial feed-lines is presented in this chapter. The system deals with two main issues: a) a path-planning model and b) a robotic-tasks scheduler. A set of kinematic control laws based on directional derivatives model the dynamical robots interaction. Distributed wheeled mobile robots perform the execution of autonomous tasks concurrently and synchronized just in time. A planner model for distributed tasks to autonomously reconfigure and synchronize online change priority missions by the robotic primitives—sense, plan, and act—are proposed. The robotic tasks concern carry-and-fetch to different goals, and dispatching materials. Numerical simulation of mathematical formulation and real experiments illustrate the parallel computing capability and the distributed robot's behavior. Results depict robots dealing with highly concurrent tasks and dynamical events through a parallel scheme.