A Contextual Approach To Learning Collaborative Behavior Via Observation

This dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate the teamwork behaviors. Machine learning techniques for learning by observation and learning by demonstration have proven successful at observing behavior of humans or other software agents and creating a behavior function for a single agent. The research described here combines current research in teamwork simulations and learning by observation to effectively train a multi-agent system in effective team behavior. The dissertation describes the background and work by others as well as a detailed description of the learning method. A prototype built to evaluate the developed approach as well as the extensive experimentation conducted is also described

Evolving Test Environments to Identify Faults in Swarm Robotics Algorithms

Swarm robotic systems are often considered to be dependable. However, there is little empirical evidence or theoretical analysis showing that dependability is an inherent property of all swarm robotic systems. Recent literature has identified potential issues with respect to dependability within certain types of swarm robotic control algorithms. However, there is little research on the testing of swarm robotic systems; this provides the motivation for developing a novel testing method for swarm robotic systems. An evolutionary testing method is proposed in this thesis to identify unintended behaviours during the execution of swarm robotic systems autonomously. Three case studies are carried out on flocking control algorithm, foraging algorithm, and task partitioning algorithm. These case studies not only show that the evolutionary testing method has the ability to identify faults in swarm robotic system, but also show that this evolutionary testing method is able to reveal failures in various swarm control algorithms. The experimental results show that the evolutionary testing method can lead to worse swarm performance and reveal more failures than the random testing method within the same number of computing evaluations. Moreover, the case study of flocking control algorithm also shows that the evolutionary testing method covers more failure types than the random testing method. In all three case studies, the dependability of each swarm robotic system has been improved by tackling the faults identified during the testing phase. Consequently, the evolutionary testing method has the potential to be used to help the developers of swarm robotic systems to design and calibrate the swarm control algorithms thereby assuring the dependability of swarm robotic systems

A review on multi-robot systems categorised by application domain

Literature reviews on Multi-Robot Systems (MRS) typically focus on fundamental technical aspects, like coordination and communication, that need to be considered in order to coordinate a team of robots to perform a given task effectively and efficiently. Other reviews only consider works that aim to address a specific problem or one particular application of MRS. In contrast, this paper presents a survey of recent research works on MRS and categorises them according to their application domain. Furthermore, this paper compiles a number of seminal review works that have proposed specific taxonomies in classifying fundamental concepts, such as coordination, architecture and communication, in the field of MRS.peer-reviewe

Improving Robot Team's performance by Passing Objects between Robots

Department of Computer Science and EngineeringA transport robot system is a robotic system in which robots move objects from one place to another place. Most existing transport robot systems perform three tasks: loading an item, moving to another location, and unloading the item. Traditional mobile robots, which carry objects one at a time, is not suitable for repeatedly transporting objects over a long distance. Therefore, in the factory or warehouse environment, they still use conveyor belts to transport a large number of objects. However, the existing conveyor belts are physically fixed in their environments, and it is difficult to reconfigure the layout of a conveyor network. In this thesis, I presente three new robotic systems that have the ability to pass objects at a distance between mobile robots. These three robotic systems are mobile conveyor belts, dynamic robot chains, and mobile workstations. First, conveyor belts are commonly used to transport many objects rapidly and effectively. I present a novel conveyor system called a mobile conveyor line that can autonomously organize itself to transport objects to a given location. In this thesis, I analyze the reachability of multiple mobile conveyor belts and present an algorithm to verify the reachability of a specified destination, as well as a way to gen- erate a configuration for connecting conveyor belts to reach the destination. The key results include a complete set of equations describing the reachable set of a mobile conveyor belt on a flat surface, which leads to an effective probabilistic strategy for autonomous configuration. The results of the experiment demonstrated the overlap effect, which states that reachable sets frequently overlap. This system can be suitable for locations where it is difficult to install a conveyor line, such as disaster zones. Second, I present to use mobile conveyor belts in foraging tasks in environments with obstacles. Foraging robots can form a dynamic robot chain network that can quickly send resources received from other foraging robots to a collecting zone called a depot area. A robot chain is essentially a sequence of mobile robots with the ability to quickly pass resources at a long distance. A dynamic robot chain network is a network of robot chains that allow the branches of the robot chains to connect multiple resource clusters. By allowing branching, the traffic near the end of the robot chain network can be dis- tributed to several branches, and congestion can be avoided. The dynamic robot chain network leverages mobility to relocate, reduce collection time for other robots, and quickly send resources received from other foraging robots to the depot area. The key result is the formation of robot chains capable of over- coming the two major limitations of existing dynamic depot foraging systems: the long travel distance for delivery and congestion near the central collection zone. In the experiments, given the same num- ber of robots, a dynamic robot chain network outperformed existing dynamic depots in multiple-place foraging problems. Third, I consider the idea of mobile workstations, which integrate mobile platforms with production machinery to improve efficiency by overlapping production time and delivery time. I describe a task planning algorithm for multiple mobile workstations and offer a model of mobile workstations and their jobs. This planning problem for mobile workstations includes the features of both traveling salesman problems (TSP) and job shop scheduling problems (JSP). For planning, I presente two algorithms: a) a complete search algorithm that offers a minimum makespan plan and b) a local search in the space of task graphs to offer suboptimal plans quickly. According to the experiments, the second algorithm can generate near-optimal temporal plans when the number of jobs is small. In addition, the second algorithm can generate noticeably shorter plans than a version of the job shop scheduling algorithm and SGPlan 5 when the number of jobs is large. This research shows that transport robot systems could work together with other robots or machines in various environments to overcome the limitations of existing systems for the environments. A mobile conveyor line can pass quickly objects at a long distance and can apply to many different environments by overcoming the existing problem of conveyor belts. By using mobile conveyor belts, the robots have the ability to pass objects at a distance between mobile robots to improve the performance of foraging tasks by overcoming the long travel distance for delivery and congestion near the central collection zone. In addition, a mobile workstation can handle the tasks that transport the production of goods to users. By paralleling the production time and the movement, a mobile workstation can substantially shorten the time it takes to deliver products to customers.ope

Interaction and Intelligent Behavior

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage