Ahmed Hussein

Multi-robot Task Allocation: A Review of the State-of-the-Art

Multi-robot systems (MRS) are a group of robots that are designed aiming to perform some collective behavior. By this collective behavior, some goals that are impossible for a single robot to achieve become feasible and attainable. There are several foreseen benefits of MRS compared to single robot systems such as the increased ability to resolve task complexity, increasing performance, reliability and simplicity in design. These benefits have attracted many researchers from academia and industry to investigate how to design and develop robust versatile MRS by solving a number of challenging problems such as complex task allocation, group formation, cooperative object detection and tracking, communication relaying and self-organization to name just a few. One of the most challenging problems of MRS is how to optimally assign a set of robots to a set of tasks in such a way that optimizes the overall system performance subject to a set of constraints. This problem is known as Multi-robot Task Allocation (MRTA) problem. MRTA is a complex problem especially when it comes to heterogeneous unreliable robots equipped with different capabilities that are required to perform various tasks with different requirements and constraints in an optimal way. This chapter provides a comprehensive review on challenging aspects of MRTA problem, recent approaches to tackle this problem and the future directions.

A comparative study between optimization and market-based approaches to multi-robot task allocation

This paper presents a comparative study between optimization-based and market-based approaches used for solving the Multirobot task allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used to find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches were extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained MRS applications that include extended number of tasks and robots. Finally, a comparative study is implemented between the two approaches and the results show that the optimization-based approach outperforms themarket-based approach in terms of optimal allocation and computational time.

Market-based approach to Multi-robot Task Allocation

This paper presents a market-based approach used for solving the Multi-robot Task Allocation (MRTA) problem that arises in the context of Multi-robot Systems (MRS). The proposed approach is used to find the best allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The approach was extensively tested over a number of test scenarios in order to test its capability of handling complex constrained MRS applications that included extended number of tasks and robots. Finally a comparative study is implemented between the proposed market-based approach and two optimization-based approaches, the results show that the optimization-based approaches outperformed the market-based approach in terms of best allocation and computational time, however, in terms of capabilities matching the difference between both algorithms is very minimal.

P2V and V2P communication for Pedestrian warning on the basis of Autonomous Vehicles

The use of smartphones in a road context by drivers and Vulnerable Road Users (VRU) is rapidly increasing. To reduce the risks related to the influence of smartphone usage in a situation where traffic needs to be considered, a collision prediction algorithm is proposed based on Pedestrian to Vehicle (P2V) and Vehicle to Pedestrian (V2P) communication technologies, which increases the visual situational awareness of VRU regarding the nearby location of both autonomous and manually-controlled vehicles in a user-friendly form. The proposed application broadcasts the devices position to the vehicles nearby, and reciprocally, the vehicles nearby broadcast their position to the device in use, supporting pedestrians and other VRU to minimize potential dangers and increase the acceptance of autonomous vehicles on our roads. Results regarding the evaluation of the proposed approach showed a good performance and high detection rate, as well as a high user satisfaction derived from the interaction with the system.

Stereo Vision-based Local Occupancy Grid Map for Autonomous Navigation in ROS

Autonomous navigation for unmanned ground vehicles has gained significant interest in the research community of mobile robotics. This increased attention comes from its noteworthy role in the field of Intelligent Transportation Systems (ITS). In order to achieve the autonomous navigation for ground vehicles, a detailed model of the environment is required as its input map. This paper presents a novel approach to recognize static obstacles by means of an on-board stereo camera and build a local occupancy grid map in a Robot Operating System (ROS) architecture. The output maps include information concerning the environment 3D structures, which is based on stereo vision. These maps can enhance the global grid map with further details for the undetected obstacles by the laser rangefinder. In order to evaluate the proposed approach, several experiments are performed in different scenarios. The output maps are precisely compared to the corresponding global map segment and to the equivalent satellite image. The obtained results indicate the high performance of the approach in numerous situations.

Autonomous off-road navigation using stereo-vision and laser-rangefinder fusion for outdoor obstacles detection

During the last decade, ground mobile robots that are able to drive autonomously in off-road environments have received a great deal of attention. Autonomous navigation in unstructured environments faces many new challenges compared to structured urban environments, these challenges increase the complexity of the localization, obstacle detection, path planning and navigation commands. Accordingly this paper presents a fusion system for stereo-vision and laser-rangefinder outdoor obstacle detection, which is implemented as an application for autonomous off-road navigation. The test platform is an electric golf-cart that is modified mechanically and electrically to operate in driver-less mode. This vehicle is equipped with binocular camera, laser-rangefinder, electronic compass and on-board embedded computer, which operates using Robotic Operating System (ROS) architecture. The proposed architecture gathers the data from all different sensors, in order to make navigation decisions from one point to another, avoiding obstacles in the path. Experimental results indicate the high performance of the proposed approaches, they show that the perception from the stereo-vision detection enhances the laser-rangefinder detection, which consequently makes a better decision in maneuvering the obstacle and returns back to the original path.

Multi-robot Task Allocation for Search and Rescue Missions

Many researchers from academia and industry are attracted to investigate how to design and develop robust versatile multi-robot systems by solving a number of challenging and complex problems such as task allocation, group formation, self-organization and much more. In this study, the problem of multi-robot task allocation (MRTA) is tackled. MRTA is the problem of optimally allocating a set of tasks to a group of robots to optimize the overall system performance while being subjected to a set of constraints. A generic market-based approach is proposed in this paper to solve this problem. The efficacy of the proposed approach is quantitatively evaluated through simulation and real experimentation using heterogeneous Khepera-III mobile robots. The results from both simulation and experimentation indicate the high performance of the proposed algorithms and their applicability in search and rescue missions.

Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles

The aim of this work is to integrate and analyze the performance of a path planning method based on Time Elastic Bands (TEB) in real research platform based on Ackermann model. Moreover, it will be proved that all modules related to the navigation can coexist and work together to achieve the goal point without any collision. The study is done by analyzing the trajectory generated from global and local planners. The software prototyping tool is Robot Operating System (ROS) from Open Source Robotics Foundation and the research platform is the iCab (Intelligent Campus Automobile) from University Carlos III. This work has been validated from a test inside the campus where the iCab has performed the navigation between the starting point and the goal point without any collision. During the experiment, we proved the low sensitivity of the TEB method to variations of the vehicle model configuration and constraints.

V2X communications architecture for off-road autonomous vehicles

Driverless vehicles have received a great deal of attention in the Intelligent Transportation Systems (ITS) research fields during the last decade. Moreover, with the release of several intelligent vehicles to the roads, the necessity of cooperation and coordination among the vehicles, infrastructure and road users is increasing. In order to implement these approaches, a proper communication architecture is required as the first step. Accordingly, this paper proposes the architecture for using three different categories of V2X communications schemes in off-road environments over multiple autonomous vehicles. The proposed schemes are inter-vehicle communication via Vehicle-To-Vehicle (V2V), bidirectional communication with pedestrians via Vehicle-To-Pedestrian (V2P) and Pedestrian-To-Vehicle (P2V), and bidirectional communication with infrastructure via Vehicle-To-Infrastructure (V2I) and Infrastructure-To-Vehicle (I2V). Over three different scenarios, numerous experiments were performed for each communication scheme. The outcome results prove the stability of the proposed schemes, in addition to their high performance over 4G connection, in terms of efficiency, in comparison to WiFi connection.

ROS-Based Approach for Unmanned Vehicles in Civil Applications

Unmanned vehicle is the term that describes any platform without a human operator on-board. These vehicles can be either tele-operated remotely through a control station, or autonomously driven using on-board sensors and controllers. With the advances in micro and nano electronics, the increase in computing efficiency, and the ability to work in dull, dirty and dangerous environments, modern unmanned vehicles aim at higher levels of autonomy. This is through development of accurate control systems and a high-level environment understanding, in order to perform complex tasks. The main part of autonomous vehicles is the navigation system, along with the supporting subsystems. The navigation system utilizes information from various sensors, in order to estimate the position and orientation of the vehicle, sense the surrounding environment and perform the correct maneuver to achieve its assigned task. Accordingly, this chapter presents a ROS-based architecture for two different unmanned vehicles to be used in civil applications, which are constrained by Size, Weight and Power (SWap). This architecture includes the algorithms for control, localization, perception, planning, communication and cooperation tasks. In addition, in order to validate the robustness of the presented vehicles, different experiments have been carried out in real world applications with two different types of Unmanned Aerial Vehicle (UAV). The experiments cover applications in various fields; for instance, search and rescue missions, environment exploration, transportation and inspection. The obtained results demonstrates the effectiveness of the proposed architecture and validates its functionality on actual platforms.