Synthesized cooperative strategies for intelligent multi-robots in a real-time distributed environment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand

In the robot soccer domain, real-time response usually curtails the development of more complex Al-based game strategies, path-planning and team cooperation between intelligent agents. In light of this problem, distributing computationally intensive algorithms between several machines to control, coordinate and dynamically assign roles to a team of robots, and allowing them to communicate via a network gives rise to real-time cooperation in a multi-robotic team. This research presents a myriad of algorithms tested on a distributed system platform that allows for cooperating multi- agents in a dynamic environment. The test bed is an extension of a popular robot simulation system in the public domain developed at Carnegie Mellon University, known as TeamBots. A low-level real-time network game protocol using TCP/IP and UDP were incorporated to allow for a conglomeration of multi-agent to communicate and work cohesively as a team. Intelligent agents were defined to take on roles such as game coach agent, vision agent, and soccer player agents. Further, team cooperation is demonstrated by integrating a real-time fuzzy logic-based ball-passing algorithm and a fuzzy logic algorithm for path planning. Keywords Artificial Intelligence, Ball Passing, the coaching system, Collaborative, Distributed Multi-Agent, Fuzzy Logic, Role Assignmen

Tele-autonomous systems: New methods for projecting and coordinating intelligent action at a distance

There is a growing need for humans to perform complex remote operations and to extend the intelligence and experience of experts to distant applications. It is asserted that a blending of human intelligence, modern information technology, remote control, and intelligent autonomous systems is required, and have coined the term tele-autonomous technology, or tele-automation, for methods producing intelligent action at a distance. Tele-automation goes beyond autonomous control by blending in human intelligence. It goes beyond tele-operation by incorporating as much autonomy as possible and/or reasonable. A new approach is discussed for solving one of the fundamental problems facing tele-autonomous systems: The need to overcome time delays due to telemetry and signal propagation. New concepts are introduced called time and position clutches, that allow the time and position frames between the local user control and the remote device being controlled, to be desynchronized respectively. The design and implementation of these mechanisms are described in detail. It is demonstrated that these mechanisms lead to substantial telemanipulation performance improvements, including the result of improvements even in the absence of time delays. The new controls also yield a simple protocol for control handoffs of manipulation tasks between local operators and remote systems

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

A passivity-based strategy for manual corrections in human-robot coaching

In recent years, new programming techniques have been developed in the human-robot collaboration (HRC) field. For example, walk-through programming allows to program the robot in an easy and intuitive way. In this context, a modification of a portion of the trajectory usually requires the teaching of the path from the beginning. In this paper we propose a passivity-based method to locally change a trajectory based on a manual human correction. At the beginning the robot follows the nominal trajectory, encoded through the Dynamical Movement Primitives, by setting high control gains. When the human grasps the end-effector, the robot is made compliant and he/she can drive it along the correction. The correction is optimally joined to the nominal trajectory, resuming the path tracking. In order to avoid unstable behaviors, the variation of the control gains is performed exploiting energy tanks, preserving the passivity of the interaction. Finally, the correction is spatially fixed so that a variation in the boundary conditions (e.g., the initial/final points) does not affect the modification

Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction

Data analytics helps basketball teams to create tactics. However, manual data collection and analytics are costly and ineffective. Therefore, we applied a deep bidirectional long short-term memory (BLSTM) and mixture density network (MDN) approach. This model is not only capable of predicting a basketball trajectory based on real data, but it also can generate new trajectory samples. It is an excellent application to help coaches and players decide when and where to shoot. Its structure is particularly suitable for dealing with time series problems. BLSTM receives forward and backward information at the same time, while stacking multiple BLSTMs further increases the learning ability of the model. Combined with BLSTMs, MDN is used to generate a multi-modal distribution of outputs. Thus, the proposed model can, in principle, represent arbitrary conditional probability distributions of output variables. We tested our model with two experiments on three-pointer datasets from NBA SportVu data. In the hit-or-miss classification experiment, the proposed model outperformed other models in terms of the convergence speed and accuracy. In the trajectory generation experiment, eight model-generated trajectories at a given time closely matched real trajectories