A petri net on-line controller for the coordination of multiple mobile robots

In applications such as mining, space exploration, and toxic waste cleanup, mobile robots are often required to move within a common environment and to share resources. This introduces the need for a means of coordinating their behaviours. Also, due to the unpredictable nature of the worksite, there is a need to accommodate changes in a dynamic environment. -- A general framework for group robotics was developed in response to this need. The framework includes a discrete event controller for on-line control and runtime monitoring, the focus of the current research. -- A Petri net based discrete event formalism has been investigated as a basis for the development of an on-line controller, ftom a high-level task description, a set of rules have been used to automatically generate a Petri net structure that provides coordinated behaviour. The Petri net can then be executed to send instructions to robots and to incorporate feedback from the robots at runtime. This on-line controller has been used to control mobile robots in a proof-of-concept demonstration. In a laboratory setting, the Petri net controller was able to coordinate the behaviour of two robots in marker-based navigation tasks. -- Although the work completed to date has provided promising results, many research challenges remain. Some suggestions for future work are presented

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Second Workshop on Modelling of Objects, Components and Agents

This report contains the proceedings of the workshop Modelling of Objects, Components, and Agents (MOCA'02), August 26-27, 2002.The workshop is organized by the 'Coloured Petri Net' Group at the University of Aarhus, Denmark and the 'Theoretical Foundations of Computer Science' Group at the University of Hamburg, Germany. The homepage of the workshop is: http://www.daimi.au.dk/CPnets/workshop02

Performance and Security Trade-offs in High-Speed Networks. An investigation into the performance and security modelling and evaluation of high-speed networks based on the quantitative analysis and experimentation of queueing networks and generalised stochastic Petri nets.

Most used security mechanisms in high-speed networks have been adopted without adequate quantification of their impact on performance degradation. Appropriate quantitative network models may be employed for the evaluation and prediction of ¿optimal¿ performance vs. security trade-offs. Several quantitative models introduced in the literature are based on queueing networks (QNs) and generalised stochastic Petri nets (GSPNs). However, these models do not take into consideration Performance Engineering Principles (PEPs) and the adverse impact of traffic burstiness and security protocols on performance. The contributions of this thesis are based on the development of an effective quantitative methodology for the analysis of arbitrary QN models and GSPNs through discrete-event simulation (DES) and extended applications into performance vs. security trade-offs involving infrastructure and infrastructure-less high-speed networks under bursty traffic conditions. Specifically, investigations are carried out focusing, for illustration purposes, on high-speed network routers subject to Access Control List (ACL) and also Robotic Ad Hoc Networks (RANETs) with Wired Equivalent Privacy (WEP) and Selective Security (SS) protocols, respectively. The Generalised Exponential (GE) distribution is used to model inter-arrival and service times at each node in order to capture the traffic burstiness of the network and predict pessimistic ¿upper bounds¿ of network performance. In the context of a router with ACL mechanism representing an infrastructure network node, performance degradation is caused due to high-speed incoming traffic in conjunction with ACL security computations making the router a bottleneck in the network. To quantify and predict the trade-off of this degradation, the proposed quantitative methodology employs a suitable QN model consisting of two queues connected in a tandem configuration. These queues have single or quad-core CPUs with multiple-classes and correspond to a security processing node and a transmission forwarding node. First-Come-First-Served (FCFS) and Head-of-the-Line (HoL) are the adopted service disciplines together with Complete Buffer Sharing (CBS) and Partial Buffer Sharing (PBS) buffer management schemes. The mean response time and packet loss probability at each queue are employed as typical performance metrics. Numerical experiments are carried out, based on DES, in order to establish a balanced trade-off between security and performance towards the design and development of efficient router architectures under bursty traffic conditions. The proposed methodology is also applied into the evaluation of performance vs. security trade-offs of robotic ad hoc networks (RANETs) with mobility subject to Wired Equivalent Privacy (WEP) and Selective Security (SS) protocols. WEP protocol is engaged to provide confidentiality and integrity to exchanged data amongst robotic nodes of a RANET and thus, to prevent data capturing by unauthorised users. WEP security mechanisms in RANETs, as infrastructure-less networks, are performed at each individual robotic node subject to traffic burstiness as well as nodal mobility. In this context, the proposed quantitative methodology is extended to incorporate an open QN model of a RANET with Gated queues (G-Queues), arbitrary topology and multiple classes of data packets with FCFS and HoL disciplines under bursty arrival traffic flows characterised by an Interrupted Compound Poisson Process (ICPP). SS is included in the Gated-QN (G-QN) model in order to establish an ¿optimal¿ performance vs. security trade-off. For this purpose, PEPs, such as the provision of multiple classes with HoL priorities and the availability of dual CPUs, are complemented by the inclusion of robot¿s mobility, enabling realistic decisions in mitigating the performance of mobile robotic nodes in the presence of security. The mean marginal end-to-end delay was adopted as the performance metric that gives indication on the security improvement. The proposed quantitative methodology is further enhanced by formulating an advanced hybrid framework for capturing ¿optimal¿ performance vs. security trade-offs for each node of a RANET by taking more explicitly into consideration security control and battery life. Specifically, each robotic node is represented by a hybrid Gated GSPN (G-GSPN) and a QN model. In this context, the G-GSPN incorporates bursty multiple class traffic flows, nodal mobility, security processing and control whilst the QN model has, generally, an arbitrary configuration with finite capacity channel queues reflecting ¿intra¿-robot (component-to-component) communication and ¿inter¿-robot transmissions. Two theoretical case studies from the literature are adapted to illustrate the utility of the QN towards modelling ¿intra¿ and ¿inter¿ robot communications. Extensions of the combined performance and security metrics (CPSMs) proposed in the literature are suggested to facilitate investigating and optimising RANET¿s performance vs. security trade-offs. This framework has a promising potential modelling more meaningfully and explicitly the behaviour of security processing and control mechanisms as well as capturing the robot¿s heterogeneity (in terms of the robot architecture and application/task context) in the near future (c.f. [1]. Moreover, this framework should enable testing robot¿s configurations during design and development stages of RANETs as well as modifying and tuning existing configurations of RANETs towards enhanced ¿optimal¿ performance and security trade-offs.Ministry of Higher Education in Libya and the Libyan Cultural Attaché bureau in Londo

A generalized laser simulator algorithm for optimal path planning in constraints environment

Path planning plays a vital role in autonomous mobile robot navigation, and it has thus become one of the most studied areas in robotics. Path planning refers to a robot's search for a collision-free and optimal path from a start point to a predefined goal position in a given environment. This research focuses on developing a novel path planning algorithm, called Generalized Laser Simulator (GLS), to solve the path planning problem of mobile robots in a constrained environment. This approach allows finding the path for a mobile robot while avoiding obstacles, searching for a goal, considering some constraints and finding an optimal path during the robot movement in both known and unknown environments. The feasible path is determined between the start and goal positions by generating a wave of points in all directions towards the goal point with adhering to constraints. A simulation study employing the proposed approach is applied to the grid map settings to determine a collision-free path from the start to goal positions. First, the grid mapping of the robot's workspace environment is constructed, and then the borders of the workspace environment are detected based on the new proposed function. This function guides the robot to move toward the desired goal. Two concepts have been implemented to find the best candidate point to move next: minimum distance to goal and maximum index distance to the boundary, integrated by negative probability to sort out the most preferred point for the robot trajectory determination. In order to construct an optimal collision-free path, an optimization step was included to find out the minimum distance within the candidate points that have been determined by GLS while adhering to particular constraint's rules and avoiding obstacles. The proposed algorithm will switch its working pattern based on the goal minimum and boundary maximum index distances. For static obstacle avoidance, the boundaries of the obstacle(s) are considered borders of the environment. However, the algorithm detects obstacles as a new border in dynamic obstacles once it occurs in front of the GLS waves. The proposed method has been tested in several test environments with different degrees of complexity. Twenty different arbitrary environments are categorized into four: Simple, complex, narrow, and maze, with five test environments in each. The results demonstrated that the proposed method could generate an optimal collision-free path. Moreover, the proposed algorithm result are compared to some common algorithms such as the A* algorithm, Probabilistic Road Map, RRT, Bi-directional RRT, and Laser Simulator algorithm to demonstrate its effectiveness. The suggested algorithm outperforms the competition in terms of improving path cost, smoothness, and search time. A statistical test was used to demonstrate the efficiency of the proposed algorithm over the compared methods. The GLS is 7.8 and 5.5 times faster than A* and LS, respectively, generating a path 1.2 and 1.5 times shorter than A* and LS. The mean value of the path cost achieved by the proposed approach is 4% and 15% lower than PRM and RRT, respectively. The mean path cost generated by the LS algorithm, on the other hand, is 14% higher than that generated by the PRM. Finally, to verify the performance of the developed method for generating a collision-free path, experimental studies were carried out using an existing WMR platform in labs and roads. The experimental work investigates complete autonomous WMR path planning in the lab and road environments using live video streaming. The local maps were built using data from live video streaming s by real-time image processing to detect the segments of the lab and road environments. The image processing includes several operations to apply GLS on the prepared local map. The proposed algorithm generates the path within the prepared local map to find the path between start-to-goal positions to avoid obstacles and adhere to constraints. The experimental test shows that the proposed method can generate the shortest path and best smooth trajectory from start to goal points in comparison with the laser simulator

Navigational Path Analysis of Mobile Robot in Various Environments

This dissertation describes work in the area of an autonomous mobile robot. The objective is navigation of mobile robot in a real world dynamic environment avoiding structured and unstructured obstacles either they are static or dynamic. The shapes and position of obstacles are not known to robot prior to navigation. The mobile robot has sensory recognition of specific objects in the environments. This sensory-information provides local information of robots immediate surroundings to its controllers. The information is dealt intelligently by the robot to reach the global objective (the target). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimisation problem and thus can be analyzed and solved using AI techniques. The optimisation of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A successful way of structuring the navigation task deals with the issues of individual behaviour design and action coordination of the behaviours. The navigation objective is addressed using fuzzy logic, neural network, adaptive neuro-fuzzy inference system and different other AI technique.The research also addresses distributed autonomous systems using multiple robot

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

A Framework for Coordinated Control of Multi-Agent Systems

Multi-agent systems represent a group of agents that cooperate to solve common tasks in a dynamic environment. Multi-agent control systems have been widely studied in the past few years. The control of multi-agent systems relates to synthesizing control schemes for systems which are inherently distributed and composed of multiple interacting entities. Because of the wide applications of multi-agent theories in large and complex control systems, it is necessary to develop a framework to simplify the process of developing control schemes for multi-agent systems. In this study, a framework is proposed for the distributed control and coordination of multi-agent systems. In the proposed framework, the control of multi-agent systems is regarded as achieving decentralized control and coordination of agents. Each agent is modeled as a Coordinated Hybrid Agent (CHA) which is composed of an intelligent coordination layer and a hybrid control layer. The intelligent coordination layer takes the coordination input, plant input and workspace input. After processing the coordination primitives, the intelligent coordination layer outputs the desired action to the hybrid layer. In the proposed framework, we describe the coordination mechanism in a domain-independent way, as simple abstract primitives in a coordination rule base for certain dependency relationships between the activities of different agents. The intelligent coordination layer deals with the planning, coordination, decision-making and computation of the agent. The hybrid control layer of the proposed framework takes the output of the intelligent coordination layer and generates discrete and continuous control signals to control the overall process. In order to verify the feasibility of the proposed framework, experiments for both heterogeneous and homogeneous Multi-Agent Systems (MASs) are implemented. In addition, the stability of systems modeled using the proposed framework is also analyzed. The conditions for asymptotic stability and exponential stability of a CHA system are given. In order to optimize a Multi-Agent System (MAS), a hybrid approach is proposed to address the optimization problem for a MAS modeled using the CHA framework. Both the event-driven dynamics and time-driven dynamics are included for the formulation of the optimization problem. A generic formula is given for the optimization of the framework. A direct identification algorithm is also discussed to solve the optimization problem

Diagnostic and adaptive redundant robotic planning and control

Neural networks and fuzzy logic are combined into a hierarchical structure capable of planning, diagnosis, and control for a redundant, nonlinear robotic system in a real world scenario. Throughout this work levels of this overall approach are demonstrated for a redundant robot and hand combination as it is commanded to approach, grasp, and successfully manipulate objects for a wheelchair-bound user in a crowded, unpredictable environment. Four levels of hierarchy are developed and demonstrated, from the lowest level upward: diagnostic individual motor control, optimal redundant joint allocation for trajectory planning, grasp planning with tip and slip control, and high level task planning for multiple arms and manipulated objects. Given the expectations of the user and of the constantly changing nature of processes, the robot hierarchy learns from its experiences in order to more efficiently execute the next related task, and allocate this knowledge to the appropriate levels of planning and control. The above approaches are then extended to automotive and space applications