Formation control of non-identical multi-agent systems

The problem considered in this work is formation control for non-identical linear multi-agent systems (MASs) under a time-varying communication network. The size of the formation is scalable via a scaling factor determined by a leader agent. Past works on scalable formation are limited to identical agents under a fixed communication network. In addition, the formation scaling variable is updated under a leader-follower network. Differently, this work considers a leaderless undirected network in addition to a leader-follower network to update the formation scaling variable. The control law to achieve scalable formation is based on the internal model principle and consensus algorithm. A biased reference output, updated in a distributed manner, is introduced such that each agent tracks a different reference output. Numerical examples show the effectiveness of the proposed method

A generalized laser simulator algorithm for mobile robot path planning with obstacle avoidance

This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment’s borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

USING COEVOLUTION IN COMPLEX DOMAINS

Genetic Algorithms is a computational model inspired by Darwin's theory of evolution. It has a broad range of applications from function optimization to solving robotic control problems. Coevolution is an extension of Genetic Algorithms in which more than one population is evolved at the same time. Coevolution can be done in two ways: cooperatively, in which populations jointly try to solve an evolutionary problem, or competitively. Coevolution has been shown to be useful in solving many problems, yet its application in complex domains still needs to be demonstrated.Robotic soccer is a complex domain that has a dynamic and noisy environment. Many Reinforcement Learning techniques have been applied to the robotic soccer domain, since it is a great test bed for many machine learning methods. However, the success of Reinforcement Learning methods has been limited due to the huge state space of the domain. Evolutionary Algorithms have also been used to tackle this domain; nevertheless, their application has been limited to a small subset of the domain, and no attempt has been shown to be successful in acting on solving the whole problem.This thesis will try to answer the question of whether coevolution can be applied successfully to complex domains. Three techniques are introduced to tackle the robotic soccer problem. First, an incremental learning algorithm is used to achieve a desirable performance of some soccer tasks. Second, a hierarchical coevolution paradigm is introduced to allow coevolution to scale up in solving the problem. Third, an orchestration mechanism is utilized to manage the learning processes

Hitchhiking Based Symbiotic Multi-Robot Navigation in Sensor Networks

Robot navigation is a complex process that involves real-time localization, obstacle avoidance, map update, control, and path planning. Thus, it is also a computationally expensive process, especially in multi-robot systems. This paper presents a cooperative multi-robot navigation scheme in which a robot can 'hitchhike' another robot, i.e., two robots going to the same (or close) destination navigate together in a leader-follower system assisted by visual servoing. Although such cooperative navigation has many benefits compared to traditional approaches with separate navigation, there are many constraints to implementing such a system. A sensor network removes those constraints by enabling multiple robots to communicate with each other to exchange meaningful information such as their respective positions, goal and destination locations, and drastically improves the efficiency of symbiotic multi-robot navigation through hitchhiking. We show that the proposed system enables efficient navigation of multi-robots without loss of information in a sensor network. Efficiency improvements in terms of reduced waiting time of the hitchhiker, not missing potential drivers, best driver-profile match, and velocity tuning are discussed. Novel algorithms for partial hitchhiking, and multi-driver hitchhiking are proposed. A novel case of hitchhiking based simultaneous multi-robot teleoperation by a single operation is also proposed. All the proposed algorithms are verified by experiments in both simulation and real environment

2011, UMaine News Press Releases

This is a catalog of press releases put out by the University of Maine Division of Marketing and Communications between January 3, 2011 and December 30, 2011

Whistleblowing for Change

The courageous acts of whistleblowing that inspired the world over the past few years have changed our perception of surveillance and control in today's information society. But what are the wider effects of whistleblowing as an act of dissent on politics, society, and the arts? How does it contribute to new courses of action, digital tools, and contents? This urgent intervention based on the work of Berlin's Disruption Network Lab examines this growing phenomenon, offering interdisciplinary pathways to empower the public by investigating whistleblowing as a developing political practice that has the ability to provoke change from within

Whistleblowing for Change

The courageous acts of whistleblowing that inspired the world over the past few years have changed our perception of surveillance and control in today's information society. But what are the wider effects of whistleblowing as an act of dissent on politics, society, and the arts? How does it contribute to new courses of action, digital tools, and contents? This urgent intervention based on the work of Berlin's Disruption Network Lab examines this growing phenomenon, offering interdisciplinary pathways to empower the public by investigating whistleblowing as a developing political practice that has the ability to provoke change from within