Tag Archives: Multirobot Systems

A ROS module that improves real-time aspects of network communication among distributed ROS machines, and a nice analysis of wireless network characteristics and limitations

December 10, 2018 16:22 ,

Danilo Tardioli, Ramviyas Parasuraman, Petter Ögren, Pound: A multi-master ROS node for reducing delay and jitter in wireless multi-robot networks, Robotics and Autonomous Systems, Volume 111, 2019, Pages 73-87, DOI: 10.1016/j.robot.2018.10.009.

The Robot Operating System (ROS) is a popular and widely used software framework for building robotics systems. With the growth of its popularity, it has started to be used in multi-robot systems as well. However, the TCP connections that the platform relies on for connecting the so-called ROS nodes presents several issues regarding limited-bandwidth, delays, and jitter, when used in wireless multi-hop networks. In this paper, we present a thorough analysis of the problem and propose a new ROS node called Pound to improve the wireless communication performance by reducing delay and jitter in data exchanges, especially in multi-hop networks. Pound allows the use of multiple ROS masters (roscores), features data compression, and importantly, introduces a priority scheme that allows favoring more important flows over less important ones. We compare Pound to the state-of-the-art solutions through extensive experiments and show that it performs equally well, or better in all the test cases, including a control-over-network example.

Posted in: Robotic software , Tagged: , , ,

A survey on decision making for multiagent systems, including multirobot systems

September 13, 2018 09:59 ,

Y. Rizk, M. Awad and E. W. Tunstel, Decision Making in Multiagent Systems: A Survey, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 3, pp. 514-529, DOI: 10.1109/TCDS.2018.2840971.

Intelligent transport systems, efficient electric grids, and sensor networks for data collection and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve common goals. Decision making is an integral part of intelligent agents and MAS that will allow such systems to accomplish increasingly complex tasks. In this survey, we investigate state-of-the-art work within the past five years on cooperative MAS decision making models, including Markov decision processes, game theory, swarm intelligence, and graph theoretic models. We survey algorithms that result in optimal and suboptimal policies such as reinforcement learning, dynamic programming, evolutionary computing, and neural networks. We also discuss the application of these models to robotics, wireless sensor networks, cognitive radio networks, intelligent transport systems, and smart electric grids. In addition, we define key terms in the area and discuss remaining challenges that include incorporating big data advancements to decision making, developing autonomous, scalable and computationally efficient algorithms, tackling more complex tasks, and developing standardized evaluation metrics. While recent surveys have been published on this topic, we present a broader discussion of related models and applications.Note to Practitioners:Future smart cities will rely on cooperative MAS that make decisions about what actions to perform that will lead to the completion of their tasks. Decision making models and algorithms have been developed and reported in the literature to generate such sequences of actions. These models are based on a wide variety of principles including human decision making and social animal behavior. In this paper, we survey existing decision making models and algorithms that generate optimal and suboptimal sequences of actions. We also discuss some of the remaining challenges faced by the research community before more effective MAS deployment can be achieved in this age of Internet of Things, robotics, and mobile devices. These challenges include developing more scalable and efficient algorithms, utilizing the abundant sensory data available, tackling more complex tasks, and developing evaluation standards for decision making.

Posted in: Artificial Intelligence , Tagged: , , ,

High performance robotic computing (HPRC) vs. High performance computing, and its application to multirobot systems

July 17, 2018 11:43 ,

Leonardo Camargo-Forero, Pablo Royo, Xavier Prats, Towards high performance robotic computing, Robotics and Autonomous Systems, Volume 107, 2018, Pages 167-181 DOI: 10.1016/j.robot.2018.05.011.

Embedding a robot with a companion computer is becoming a common practice nowadays. Such computer is installed with an operatingsystem, often a Linux distribution. Moreover, Graphic Processing Units (GPUs) can be embedded on a robot, giving it the capacity of performing complex on-board computing tasks while executing a mission. It seems that a next logical transition, consist of deploying a cluster of computers among embedded computing cards. With this approach, a multi-robot system can be set as a High Performance Computing (HPC) cluster. The advantages of such infrastructure are many, from providing higher computing power up to setting scalable multi-robot systems. While HPC has been always seen as a speeding-up tool, we believe that HPC in the world of robotics can do much more than simply accelerating the execution of complex computing tasks. In this paper, we introduce the novel concept of High Performance Robotic Computing — HPRC, an augmentation of the ideas behind traditional HPC to fit and enhance the world of robotics. As a proof of concept, we introduce novel HPC software developed to control the motion of a set of robots using the standard parallel MPI (Message Passing Interface) library. The parallel motion software includes two operation modes: Parallel motion to specific target and swarm-like behavior. Furthermore, the HPC software is virtually scalable to control any quantity of moving robots, including Unmanned Aerial Vehicles, Unmanned Ground Vehicles, etc.

Posted in: Computer science, Robotic software , Tagged: ,