Soccer Robot Localization Based on Sensor Fusion From Odometry and Omnivision

Purpose– The purpose of this paper is to design intelligent robots operating in such dynamic environments like the RoboCup Middle-Size League (MSL). In the RoboCup MSL, two teams of five autonomous robots play on an 18- × 12-m field. Equipped with sensors and on-board computers, each robot should be able to perceive the environment, make decision and control itself to play the soccer game autonomously.Design/methodology/approach– This paper presents the design of our soccer robots, participating in RoboCup MSL. The mechanical platform, electrical architecture and software framework are discussed separately. The mechanical platform is designed modularly, so easy maintainability is achieved; the electronic architecture is built on industrial standards using PC-based control technique, which results in high robustness and reliability during the intensive and fierce MSL games; the software is developed upon the open-source Robot Operating System (ROS); thus, the advantages of ROS such as modularity, portability and expansibility are inherited.Findings– Based on this paper and the open-source hardware and software, the MSL robots can be re-developed easily to participate in the RoboCup MSL. The robots can also be used in other research and education fields, especially for multi-robot systems and distributed artificial intelligence. Furthermore, the main designing ideas proposed in the paper, i.e. using a modular mechanical structure, an industrial electronic system and ROS-based software, provide a common solution for designing general intelligent robots.Originality/value– The methodology of the intelligent robot design for highly competitive and dynamic RoboCup MSL environments is proposed.

Ball handling and dribbling are basics soccer techniques. Therefore, handling and dribbling mechanism becomes important component for MSL (Middle Size League) robot soccer. The implementation of active handling and dribbling systems aims to enable the robot to perform dribbling mechanism when maneuvering, while in idle position use handling mechanism. Feedforward and feedback applied as control method in active ball handling and dribbling system. Feedforward control method will give a response to the dribbler wheel based on the motion of robot movement against x , y , and θ axes. Whereas, Feedback control method applied fuzzy inference system by reading the displacement of the arm angle from sensor databased on angle reference of dribbler arm to keep the ball in an ideal position. Implementation geometric calculation for feedforward control toward x , y , and θ axes movement of the robot that it has resulted RMSE value for left motor is 5.75 and for right motor is 7.01. Fuzzy inference system method is used as a feedback control to manipulate the speed and direction of wheel’s rotation as a result ball remains in the ideal position. The value of arm angle displacement and the combination of the two-dribbler arms are used as input for fuzzy inference system. The results of this research show that the application of the combined feedforward and feedback control methods in active handling and dribbling systems can provide robot-dribbling capabilities. Consequently, the robot has a good ability in handling and dribbling mechanism.

Self-localization is the basis to realize autonomous ability such as motion planning and decision-making for mobile robots, and omnidirectional vision is one of the most important sensors for RoboCup Middle Size League (MSL) soccer robots. According to the characteristic that RoboCup competition is highly dynamic and the deficiency of the current self-localization methods, a robust and real-time self-localization algorithm based on omnidirectional vision is proposed for MSL soccer robots. Monte Carlo localization and matching optimization localization, two most popular approaches used in MSL, are combined in our algorithm. The advantages of these two approaches are maintained, while the disadvantages are avoided. A camera parameters auto-adjusting method based on image entropy is also integrated to adapt the output of omnidirectional vision to dynamic lighting conditions. The experimental results show that global localization can be realized effectively while highly accurate localization is achieved in real-time, and robot self-localization is robust to the highly dynamic environment with occlusions and changing lighting conditions.

Real-time and accurate robot detection and localization is important for the RoboCup Middle size League (MSL) soccer robots. In the current robot detection methods used by most of the teams, the black-color-based information is used to distinguish robots from the environment, which is not robust if the robot changes its makers' color according to the current rule. Considering the good performance of deep learning on the feature extraction and object detection, in this paper, we propose a novel approach for robot detection and localization based on Convolutional Neural Networks (CNNs) for RoboCup MSL soccer robots. The approach is composed of two stages: robot detection using the RGB image, and robot localization using the depth point cloud. The high accuracy and mean average precision (mAP) verify that the proposed method is suitable for robot detection during the MSL competition, which will benifit the following strategy design and obstacle avoidance procedures. The proposed approach can be easily exploited to deal with different objects and adapted to be used in other RoboCup leagues. The acquired dataset is made available for the community.

There are many ways to build the software for a Middle Size League (MSL) robot. A common problem in developing software for any long-term project including MSL is enabling future developers to maintain and continue further research on the project. A solution to this problem is to use an already existing software development framework. In recent years, the Robot Operating System (ROS) has become the ’de facto’ standard framework for robotics software development. This paper will explore the advantages found in using ROS as the framework to build a software for a robot The three main advantages that were found in using ROS are data sharing, hardware abstraction, and program concurrency. These advantages allowed the team to build modular software that is more maintainable. Furthermore, there was minimum computation cost found in using ROS. Lastly, the locomotion system was tested. When given a linear movement command, the locomotion system has an average actual distance error of 8.84 cm with a standard deviation of 6.70 cm. It is also found that the average computed error is 3.54 cm with a standard deviation of 1.49 cm.

Object detection and localization is a paramount important and challenging task in RoboCup MSL (Middle Size League). It has a strong constraint on real-time, as both the robot and obstacles (also robots) are moving quickly. In this paper, a real-time object segmentation approach is proposed, based on a RGB-D camera in which only the range information has been used. The method has four main steps, e.g., point cloud filtering, background points removing, clustering and object localization. Experimental results show that the proposed algorithm can effectively detect and segment objects in 3D space in real-time.

Robotic soccer is nowadays a popular research domain in the area of multi-robot systems. RoboCup is an international joint project to promote research in artificial intelligence, robotics and related fields. RoboCup chose soccer as the main problem aiming at innovations to be applied for socially relevant problems. It includes several competition leagues, each one with a specific emphasis, some only at software level, others at both hardware and software, with single or multiple agents, cooperative and competitive. In the context of RoboCup, the Middle Size League (MSL) is one of the most challenging. In this league, each team is composed of up to 5 robots with a maximum size of 50cm× 50cm, 80cm height and a maximumweight of 40Kg, playing in a field of 18m× 12m. The rules of the game are similar to the official FIFA rules, with minor changes required to adapt them for the playing robots CAMBADA, Cooperative Autonomous Mobile roBots with Advanced Distributed Architecture, is the MSL Soccer team from the University of Aveiro. The project started in 2003, coordinated by the Transverse Activity on Intelligent Robotics group of the Institute of Electronic and Telematic Engineering of Aveiro (IEETA). This project involves people working on several areas for building the mechanical structure of the robot, its hardware architecture and controllers (Almeida et al., 2002; Azevedo et al., 2007) and the software development in areas such as image analysis and processing (Caleiro et al., 2007; Cunha et al., 2007; Martins et al., 2008; Neves et al., 2007; 2008), sensor and information fusion (Silva et al., 2008; 2009), reasoning and control (Lau et al., 2008), cooperative sensing approach based on a Real-Time Database (Almeida et al., 2004), communications among robots (Santos et al., 2009; 2007) and the development of an efficient basestation. The main contribution of this chapter is to present the new advances in the areas described above involving the development of an MSL team of soccer robots, taking the example of the CAMBADA team that won the RoboCup 2008 and attained the third place in the last edition of the MSL tournament at RoboCup 2009. CAMBADA also won the last three editions

Middle-size robot soccer is one of the divisions that competed in national events such as the National Indonesia Robotics Competition and international competitions such as the middle size league (MSL). One of the main components in soccer robots is the kicker system. The kicker system is expected to be high torque, robust, and safe. In this work, a high voltage kicker system is designed and evaluated to substitute ROSTU's previous kicker system. This high voltage solenoid-based kicker system works at 380V and uses the electromagnetic force principle to move a ball. The performance criteria of the kicker system are it can move a ball with a mass of around 1 kg for a minimum range of 3 m and control the charging and discharging process in high voltage conditions. The experiment results show that the kicker system can move a ball with a mass of 1.06 kg, a difference kick distance from 100cm to 350cm, and a monitoring system that can show information about the capacitor voltage and system status.

In this paper, we describe the concept, design and implementation of a series of autonomous mobile soccer robots, named "Musashi" robot, which have a mechatronics modular architecture, to participate in the RoboCup middle-size league. In this design methodology, we show that the selection of a proper moving mechanism, a suitable vision system and a mechatronics modular architecture design can lead to realize a reliable, simple, and low cost robot comparing with most of car-like robots including many kinds of sensors and a complex design structure.

Robotic soccer is a challenging research domain because many different research areas have to be addressed in order to create a successful team of robot players. The paper presents the CS Freiburg team, the winner in the middle-size league at RoboCup 1998, 2000, and 2001. The paper focuses on multiagent coordination for both perception and action. The contributions of the paper are new methods for tracking ball and players observed by multiple robots, team coordination methods for strategic team formation and dynamic role assignment; a rich set of basic skills allowing robots to respond to a large range of situations in an appropriate way, and an action-selection method based on behavior networks, as well as a method to learn the skills and their selection. As demonstrated by evaluations of the different methods and by the success of the team, these methods permit the creation of a multirobot group which is able to play soccer successfully. In addition, the developed methods promise to advance the state of the art in the multirobot field.

Autonomous navigation technologies are continuously improving in terms of performance and safety. A key subfield of autonomous navigation for mobile robots is localization or precise positioning, involving the robot understanding its position relative to key points of interest in the environment. Traditionally, localization is purely dependent on the quality of input received from onboard sensors and therefore localization quality degrades in the presence of high sensor error or other faults. Sensor fusion is useful to combat this. Sensor fusion is used to combine various data types from different sensors to gain a lower overall uncertainty or error in the resulting data, when compared to the separate data from each individual sensor. In this study a four-wheel mobile robot with front-wheel driving and steering is localized during navigation. The investigation is performed in simulation and with a physical prototype. The mobile robot has several sensors including a wheel encoder on each wheel, an inertial measurement unit as well as an indoor GPS system. A designed Kalman Filter called The Combined Kalman Filter has been designed to fuse the sensor input data in order to output the position data required to localize the robot in (X, Y) cartesian coordinates. This is completed in the presence of sensor bias error and noise with the output path compared against a pre-selected ground truth trajectory. The proposed filter is based on the Extended Kalman Filter to contend with the non-linear model arising from the four-wheel steerable robot with the steering system following the Ackermann steering condition. The robot model and sensors are simulated in MATLAB for a chosen ground truth trajectory with results highlighting any detected faults and final accurate position information. It was seen that the estimated filter output had high accuracy when compared with the ground truth. Compared with other investigations the proposed filter has reduced mean error and minimal deviations compared with the ground truth path. In addition, this filtering model can be used in general for the localization of other actuated steering vehicles and aims to widen the research in this field as the majority of research in this area leverages differential drive robots which require less complex modeling.

This paper describes a topological navigation system, based on the description of key-places by a reduced number of parameters that represent images associated to specific locations in configuration space, and the application of the developed system to robotic soccer, through the implementation of the developed algorithms to RoboCup Middle-Size League (MSL) robots, under the scope of the SocRob project (Soccer Robots or Society of Robots). A topological map is associated with a graph, where each node corresponds to a key-place. Using this approach, navigation is reduced to a graph path search. Principal Components Analysis was used to represent key-places from pre-acquired images and to recognize them at navigation time. The method revealed a promising performance navigating between key-places and proved to adapt to different graphs. Furthermore, it leads to a robot programming language based on qualitative descriptions of the target locations in configuration space (e.g., Near Blue Goal with the Goal on its Left). Simulation results of the method application are presented, using a realistic simulator.

Vision based robot soccer localization system, aiming to create a robot soccer localization system. The implementation of this idea is motivated by the need of the localization system for the robot soccer to play the ball effectively and efficiently. The expected result of this research is to develop the localization system for the robot soccer based on camera vision. Thus the developed robot will be able to play football better. It is because of the localization system that can make the robot able to behave more precisely on certain condition and location. For example, the robot should be able to find the ball in its own area or in the opponent’s. Research conducted on several major parts, consisting: image capture, processing image information and directions to the information about the location of the robot at the time. The experimental results indicate that the localization system in this research work has the ability to perform an approximation of the robot location with respect to the goal.

To achieve an accurate and robust localization result under Global Navigation Satellite System (GNSS) denied environment, coupling GNSS with other sensors tends to be a promising solution, also known as the sensor fusion system. One of the most challenging scenarios in sensor fusion is to couple vision navigation system with GNSS, especially the tightly-coupled fusion. Compared with those loosely coupled ones, it performs the information fusion on a more intrinsic level, which can benefit the accuracy and robustness of the system. However, the major barrier lying in front of tightly coupling method is the way to unify the heterogeneous and asynchronous GNSS observations with vision observations. To solve this problem, we start from the Vison Simultaneous Localization and Mapping (V-SLAM) method, which has already become the mainstream in vision navigation domain. By utilizing existing attempts on graph-optimization-based GNSS data processing, we propose a joint graph optimization combining vision-SLAM with GNSS pseudorange. It is a tightly coupled fusion of GNSS and stereo camera, implemented by considering GNSS satellites as vision feature points. Based on the framework of mature state-of-art pose-graph SLAM, a graph optimization is applied simultaneously on GNSS and vision positioning, to achieve a robust, accurate, yet versatile fusion system.

In this study, we present a socially aware navigation framework for mobile service robots by integrating the systems proposed in our previous studies into a completed mobile robot navigation system, including the human detection and tracking system that is used to detect and track humans; the encoder and laser-based localization and mapping system that is used to determine the position of the robot in the environment; the approaching pose estimation system that is used to estimate the approaching pose of the robot to the human; and the social timed elastic band-based motion planning system that is used to socially navigate the mobile robots to approach the human. In addition, in the paper we describe in detail the motor control system and the design of our mobile robot platform, which is then utilized to conduct experiments in the real-world environments. We verify the feasibility and usefulness of the proposed socially aware robot navigation framework through a series of experiments in a corridor-like environment. The experimental results show that our proposed framework is able to drive the mobile robots to both avoid and approach the humans, providing the safety and comfort for the humans and socially acceptable behaviors for the mobile service robots in the dynamic social environments.