Maram Alajlan

Global path planning for mobile robots in large-scale grid environments using genetic algorithms

Global path planning is considered as a fundamental problem for mobile robots. In this paper, we investigate the capabilities of genetic algorithms (GA) for solving the global path planning problem in large-scale grid maps. First, we propose a GA approach for efficiently finding an (or near) optimal path in the grid map. We carefully designed GA operators to optimize the search process. We also conduct a comprehensive statistical evaluation of the proposed GA approach in terms of solution quality, and we compare it against the well-known A* algorithm as a reference. Extensive simulation results show that GA is able to find the optimal paths in large environments equally to A* in almost all the simulated cases.

Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments

This article presents the results of the 2-year iroboapp research project that aims at devising path planning algorithms for large grid maps with much faster execution times while tolerating very small slacks with respect to the optimal path. We investigated both exact and heuristic methods. We contributed with the design, analysis, evaluation, implementation and experimentation of several algorithms for grid map path planning for both exact and heuristic methods. We also designed an innovative algorithm called relaxed A-star that has linear complexity with relaxed constraints, which provides near-optimal solutions with an extremely reduced execution time as compared to A-star. We evaluated the performance of the different algorithms and concluded that relaxed A-star is the best path planner as it provides a good trade-off among all the metrics, but we noticed that heuristic methods have good features that can be exploited to improve the solution of the relaxed exact method. This led us to design new hybrid algorithms that combine our relaxed A-star with heuristic methods which improve the solution quality of relaxed A-star at the cost of slightly higher execution time, while remaining much faster than A* for large-scale problems. Finally, we demonstrate how to integrate the relaxed A-star algorithm in the robot operating system as a global path planner and show that it outperforms its default path planner with an execution time 38% faster on average.

COROS: A Multi-Agent Software Architecture for Cooperative and Autonomous Service Robots

Building distributed applications for cooperative service robots systems is a very challenging task from software engineering perspective. Indeed, apart from the complexity of designing software components for the control of a single autonomous robot, cooperative multi-robot systems require additional care in the design of software components to ensure communication and coordination between the robotic agents. This chapter proposes COROS, a new multi-agent software architecture for cooperative and autonomous service robots with the objective to make easier the design and development of multi-robot applications. We present a high-level conceptual architecture for multi-agent robotics systems that represents a generic framework for cooperative multi-robot applications. Furthermore, we present an instantiation of this generic architecture with an implementation software architecture on top of the Robot Operating System (ROS) middleware. The proposed concrete software architecture follows a component-based approach to ensure modularity, software reuse, extensibility and scalability of the multi-robot operational software. In addition, one major added value of our architecture is that it provides a tangible solution to supporting multi-robot software development for the ROS middleware, as ROS was originally designed for single-robot applications. We also demonstrate a sample of real-world case studies of cooperative and autonomous service robots applications in an office-like environment, including discovery and courier delivery applications.

Turtlebot at Office: A Service-Oriented Software Architecture for Personal Assistant Robots Using ROS

This paper presents the design of an assistive mobile robot to support people in their everyday activities in office and home environments. The contribution of this paper consists in the design of a modular component-based software architecture that provides different abstraction layers on top of Robot Operating System (ROS) to make easier the design and development of service robots with ROS. The first abstraction layer is the COROS framework composed of complementary software subsystems providing different interfaces between ROS and the client applications. The second abstraction layer is the integration of Web services into ROS to allow client applications to seamlessly and transparently interact with the robot while hiding all implementation details. The proposed software architecture was validated through a experimental prototype of Turtlebot deployed in University campus. Furthermore, we outline the challenges incurred during experimentation and focus on lessons learned throughout the implementation and deployment.

Supporting Seamless Mobility for Real-Time Applications in Named Data Networking

Abstract Named Data Networking architecture originally provided consumer mobility by design, however content or producer mobility was left unspecified. Since then a number of producer mobility support schemes have been proposed. In this paper, we provide a survey on the most relevant proposed techniques to support mobility in NDN. We classify these mobility support techniques into categories based on their underlying mechanisms of explicit notification, routing, indirection, mapping, and proactive caching. We discuss their strengths and weaknesses, and investigate their appropriateness to accommodate real time requirements necessitated by most of today traffic.

ROSLink: Bridging ROS with the Internet-of-Things for Cloud Robotics

The integration of robots with the Internet is nowadays an emerging trend, as new form of the Internet-of-Things (IoT). This integration is crucially important to promote new types of cloud robotics applications where robots are virtualized, controlled and monitored through the Internet. This paper proposes ROSLink, a new protocol to integrate Robot Operating System (ROS) enabled-robots with the IoT. The motivation behind ROSLink is the lack of ROS functionality in monitoring and controlling robots through the Internet. Although, ROS allows control of a robot from a workstation using the same ROS master, however this solution is not scalable and rather limited to a local area network. Solutions proposed in recent works rely on centralized ROS Master or robot-side Web servers sharing similar limitations. Inspired from the MAVLink protocol, the proposed ROSLink protocol defines a lightweight asynchronous communication protocol between the robots and the end-users through the cloud. ROSLink leverages the use of a proxy cloud server that links ROS-enabled robots with users and allows the interconnection between them. ROSLink performance was tested on the cloud and was shown to be efficient and reliable.

Robot Path Planning and Cooperation - Foundations, Algorithms and Experimentations

This book presents extensive research on two main problems in robotics: the path planning problem and the multirobot task allocation problem. It is the first book to provide a comprehensive solution for using these techniques in large-scale environments containing randomly scattered obstacles. The research conducted resulted in tangible results both in theory and in practice. For path planning, new algorithms for large-scale problems are devised and implemented and integrated into the Robot Operating System (ROS). The book also discusses the parallelism advantage of cloud computing techniques to solve the path planning problem, and, for multi-robot task allocation, it addresses the task assignment problem and the multiple traveling salesman problem for mobile robots applications. In addition, four new algorithms have been devised to investigate the cooperation issues with extensive simulations and comparative performance evaluation. The algorithms are implemented and simulated in MATLAB and Webots. Studies in Computational Intelligence

Different Approaches to Solve the MRTA Problem

The multi-robot task allocation problem is a fundamental problem in robotics research area. The problem roughly consists of finding an optimal allocation of tasks among several robots to reduce the mission cost to a minimum. As mentioned in Chap. 6, extensive research has been conducted in the area for answering the following question: Which robot should execute which task? In this chapter, we design different solutions to solve the MRTA problem. We propose four different approaches: an improved distributed market-based approach (IDMB), a clustering market-based approach (CM-MTSP), a fuzzy logic-based approach (FL-MTSP), and Move-and-Improve approach. These approaches must define how tasks are assigned to the robots. The IDBM, CM-MTSP, and Move-and-Improve approaches are based on the use of an auction process where bids are used to evaluate the assignment. The FL-MTSP is based on the use of the fuzzy logic algebra to combine objectives to be optimized.

Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot

The multi-robot task allocation is a fundamental problem in robotics research area. Indeed, robots are typically intended to collaborate together to achieve a given goal. This chapter studies the performance of the IDBM, CM-MTSP, FL-MTSP, and Move-and-Improve approaches. In order to highlight the performance of the proposed schemes, we compared each one to appropriate existing ones. IDMB was compared with the RTMA [1], CM-MTSP was compared with single-objective and greedy algorithms, and FL-MTSP was compared with a centralized approach based on genetic algorithm and with NSGA-II algorithm. To validate the efficiency of the Move-and-Improve distributed algorithm, we first conducted extensive simulations and evaluated its performance in terms of the total traveled distance and the ratio of overlaped targets under different settings. The simulation results show that IDMB and Move-and-Improve algorithms produce near-optimal solutions. Also, CM-MTSP and FL-MTSP provide a good trade-off between conflicting objectives.

Introduction to Mobile Robot Path Planning

Robotic is now gaining a lot of space in our daily life and in several areas in modern industry automation and cyber-physical applications. This requires embedding intelligence into these robots for ensuring (near)-optimal solutions to task execution. Thus, a lot of research problems that pertain to robotic applications have arisen such as planning (path, motion, and mission), task allocation problems, navigation, tracking. In this chapter, we focused on the path planning research problem.