Robot In Unknown Environment Research Articles

Trajectory Planning and Collision Avoidance Algorithm for Mobile Robotics System

The field of autonomous mobile robotics has recently gained many researchers’ interests. Due to the specific needs required by various applications of mobile robot systems, especially in navigation, designing a real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. Therefore, an efficient collision avoidance and path following methodology is needed to develop an intelligent and effective autonomous mobile robot system. This paper introduces a new technique for line following and collision avoidance in the mobile robotic systems. The proposed technique relies on the use of low-cost infrared sensors, and involves a reasonable level of calculations, so that it can be easily used in real-time control applications. The simulation setup is implemented on multiple scenarios to show the ability of the robot to follow a path, detect obstacles, and navigate around them to avoid collision. It also shows that the robot has been successfully following very congested curves and has avoided any obstacle that emerged on its path. Webots simulator was used to validate the effectiveness of the proposed technique.

Path Planning of Mobile Robot Using Fuzzy-Potential Field Method

This paper deals with the navigation of a mobile robot in unknown environment using artificial potential field method. The aim of this paper is to develop a complete method that allows the mobile robot to reach its goal while avoiding unknown obstacles on its path. An approach proposed is introduced in this paper based on combing the artificial potential field method with fuzzy logic controller to solve drawbacks of artificial potential field method such as local minima problems, make an effective motion planner and improve the quality of the trajectory of mobile robot.

Neural network based FastSLAM for autonomous robots in unknown environments

Map learning and self-localization based on perception of the environment’s structure are fundamental capacities required for intelligent robots to realize true autonomy. Simultaneous Localization and Mapping (SLAM) is an effective technique for such robots, as it addresses the problem of incrementally building an environment map from noisy sensory data and tracking the robot’s path with the built map. As a popular SLAM solution, FastSLAM suffers from limitation on error accumulation introduced by incorrect odometry model and inaccurate linearization of the SLAM nonlinear functions. To overcome the problem, a new Jacobian free neural network (NN) based FastSLAM algorithm is derived and discussed in this paper. The main contribution of the algorithm is twofold: on the one hand, the odometry error is online compensated by using a multilayer NN, and the NN is online trained during the SLAM process; on the other hand, the third-degree Cubature rule for Gaussian weighted integral, which calculates nonlinear transition density of Gaussian prior up to the 3rd order nonlinearity, is utilized to estimate the SLAM state (i.e., the robot path and environment map) and to online train the NN compensator. The performance of proposed SLAM is investigated and compared with that of popular FastSLAM2.0 in simulations and experiments. Results show that the proposed method improves the SLAM performance.

Navigation of autonomous mobile robot using different activation functions of wavelet neural network

Abstract An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network.

Spiking neural network-based target tracking control for autonomous mobile robots

In this paper, a target tracking controller based on spiking neural network is proposed for autonomous robots. This controller encodes the preprocessed environmental and target information provided by CCD cameras, encoders and ultrasonic sensors into spike trains, which are integrated by a three-layer spiking neural network (SNN). The outputs of SNN are generated based on the competition between the forward/backward neuron pair corresponding to each motor, with the weights evolved by the Hebbian learning. The application to target tracking of a mobile robot in unknown environment verifies the validity of the proposed controller.

Comparison of GSA, SA and PSO Based Intelligent Controllers for Path Planning of Mobile Robot in Unknown Environment

Now-a-days autonomous mobile robots have found applications in diverse fields. An Autonomous robot system must be able to behave in an intelligent manner to deal with complex and changing environment. This work proposes the performance of path planning and navigation of autonomous mobile robot using Gravitational Search Algorithm (GSA), Simulated Annealing (SA) and Particle Swarm optimization (PSO) based intelligent controllers in an unstructured environment. The approach not only finds a valid collision free path but also optimal one. The main aim of the work is to minimize the length of the path and duration of travel from a starting point to a target while moving in an unknown environment with obstacles without collision. Finally, a comparison is made between the three controllers. It found that the path length and time duration made by the robot using GSA is better than SA and PSO based controllers for the same work Key-Words: Autonomous Mobile Robot; Collision Free Path; Gravitational Search Algorithm; Particle Swarm Optimization; Simulated Annealing Algorithm; Path Planning

Environment exploration and map building of mobile robot in unknown environment

Simultaneous environment exploration and map building of a mobile robot in an unknown environment are studied. Based on the real time data acquired from a laser sensor, a suitable environment exploration strategy with obstacle avoidance ability is proposed. To handle the problems existing in producing and evaluating candidates, feasible approaches are proposed. They can maximise the expected information gain and keep the environment information integrated, and ensure the environment exploration's continuum and complete traversal. The new evaluation method overcomes the drawbacks (attend to one criterion and lose another) of the traditional weighted average method. It can comprehensively evaluate the travelling cost, expected information gain and rotating angle to guarantee the quality of the optimal candidate. Furthermore, a topological map model is proposed which uses the nodes of the growing neural gas network as the topological network nodes. Through the growing characteristic of the GNG network, new topological nodes are added into the network to abstract and express the holistic knowledge of the surrounding environment and construct the environment map. Simulation results of two different indoor environments demonstrate its effectiveness and feasibility.

漸進的生成地図に基づく掃引ロボットのための効率的動作生成

This paper discusses the efficiency and completeness of a sweeping task to ensure the reliability of sweeping robots. The map information is necessary in order to ensure the completeness of sweeping, but it is not practical to give the preliminary map information to the robot. When the robot generates the map online, the reliability of the map is low in the beginning of the sweeping task. Therefore, the robot cannot use globally motion control. In the latter half of sweeping, unswept area are scattered like detached lands. The map has high reliability at this time, and the motion control using the whole map information is effective. The reliability of the map is gradually increased during the sweeping task, the robot should switch the motion control from locally to globally in accordance with it. In this paper, we propose the new motion control method for a sweeping robot in unknown environments. The robot selects mainly local motion control method at an early stage in this algorithm. As time goes on, the probability of the global motion control method selecting is increased. Even though the robot does not know the area of environment, the robot behaves as the above. Simulation and experimental results show the proposed method is more effective than the behavior-based method.

Mobile Robot Navigation using Fuzzy Logic and Wavelet Network

This paper presents the proposed autonomous mobile robot navigation scheme. The navigation of mobile robot in unknown environment with obstacle avoidance is based on using fuzzy logic and wavelet network. Several cases are designed and modeled in Simulink and MATLAB. Simulation results show good performance for the proposed scheme.

Simple Image Processing Algorithms for Robot Navigation in Unknown Environment

This article deals with simple image processing algorithms which are used for navigation of the robot in unknown environment. In the beginning of the article image processing procedures used in these algorithms are defined. The transformation of coordinate system of camera to robot’s coordinate system is introduced. The main body of the article consists of the definition of L-K optical flow method used in proposed visual odometry system. Article also contains the parameter settings of the used methods. Emphasis in these algorithms has been placed on simplicity and speed, so that they can be carried out in real-time. Algorithms have been verified on several scenarios.

Mobile Robot Navigation using Fuzzy Limit-Cycles in Cluttered Environment

— This paper proposes a hybrid approach based on limit-cycles method and fuzzy logic controller for the problem of obstacle avoidance of mobile robots in unknown environment. The purpose of hybridization consists on the improvement of basic limit-cycle method in order to obtain safe and flexible navigation. The proposed algorithm has been successfully tested in different configurations on simulation.

Fuzzy Logic Control Based Navigation Research for Mine Rescue Robot in Unknown Environments

This paper proposes the research significance of mine rescue robot which can assist rescuers to enter into the disaster site, and can use its own merits to explore the environments and to reach the location of the trapped workers quickly. It reduces the danger of accidents, and improves aid efficiency. This paper summarizes the status of mine rescue robot, and compares with commonly used external sensors. Ultrasonic ranging sensors are selected as part of the ranging system. In addition, navigation research for mine robot is the key in this paper. According to the features of robots working environments which is unknown and uncertain, fuzzy logic control, a simple robustness navigation controlling algorithm with fault toleration, is selected to achieve navigation mission. The experimental results show that it achieves the desired effect.

MULTISENSOR DATA FUSION BASED AUTONOMOUS MOBILE ROBOT WITH MANIPULATOR FOR TARGET DETECTION

This paper proposes a novel autonomous mobile robot in unknown environment navigating through obstacle s by computing the shortest path. Flood fill algorithm is used for pat h planning of the mobile robot. The concept of mini mum energy contour to perform the desired operation of robotic manipulator is ach ieved using Fuzzy algorithm. Object detection is do ne by utilizing a mobile robot with sensors and object recognition is achieved by image processing using Principal Component Analysis (PCA). This paper describes the implementation of multi sensor data f usion assisting a mobile robot to acquire a purposi ve behavior in the respective environment. This is achieved by directly integrati ng sensor information which helps the robot to succ essfully navigate and also enables fetch & retrieval operation of robotic mani pulator. In this approach, information is taken fro m distance sensors, position sensors and image sensor. This work also aims to pr ovide an optimal fusion of information from distrib uted multiple sensors using Kalman Filter.

Collision avoidance control for a human-operated four-wheeled mobile robot

Because the collision avoidance function is indispensable for providing safe and easy operation of human-operated robotic systems, this paper deals with the collision avoidance control for a human-operated mobile robot in unknown environments. A typical four-wheeled mobile robot with infrared distance sensors for detecting obstacles is considered. The robot cannot move in an arbitrary direction owing to a nonholonomic constraint. Therefore, we propose a simple control approach in which a human operator’s control input is modified in real time to satisfy the nonholonomic constraint and avoid collision with obstacles. The proposed controller has steering- and brake-like functions that are adjusted according to the distance sensor information. The stability of the proposed control system is analyzed with a linear model. The effectiveness of the proposed method is confirmed by experiments in which several operators control the robot in an environment with obstacles.

Challenging of Path Planning Algorithms for Autonomous Robot in Unknown Environment

Challenging of Path Planning Algorithms for Autonomous Robot in Unknown Environment

Autonomous Variable‐Resolution Map Building for Mobile Robots in Unknown Environments

SUMMARYThis paper presents a method that allows simultaneous map building and path planning for mobile robots in unknown environments. A graphical representation of a workspace in variable resolutions is constructed using measurement data obtained by omnidirectional distance sensors. At the same time, a search for a feasible path to the target destination is executed using the constructed graph map. The proposed method is evaluated by performing simulations and experiments using an omnidirectional mobile robot equipped with laser range finders. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(4): 59–69, 2014; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22493

Mobile Robot Navigation using Wavelet Network and Fuzzy Logic

This paper present the proposed autonomous mobile robot navigation scheme. The navigation of mobile robot in unknown environment with obstacle avoidance is based on using fuzzy logic and wavelet network. Several cases are designed and modeled in Simulink and MATLAB. Simulation results show good performance for the proposed scheme.

Robust and Efficient Slam Via Compressed H∞ Filtering

AbstractSimultaneous localization and mapping (SLAM) is an important and challenging task for the operation of autonomous mobile robots in which both the pose of the robot and features of the environment need to be estimated at the same time. In particular, it is desirable to achieve robustness and efficiency in the SLAM implementation. Robots in unknown environment are likely to be subject to modeling errors which cannot be easily characterized in terms of statistical properties. To mitigate the effect of uncertainties and disturbances, robust filters such as the H∞ filter can be employed. However, robust filters are complex to implement, demanding a significant amount of computational resources. This study proposes a compressed H∞ filter to solve the robust SLAM problem in which robot dynamics are subject to uncertainties and measurements are subject to bounded‐but‐unknown disturbances. To achieve an efficient implementation, the state is partitioned into active and inactive states where the latter refers to state variables which are invariant and independent of the measurement at the epoch. With such a partitioning, the active state represents the robot pose and locations of landmarks inside a certain area surrounding the robot. The computational load is reduced since only active state needs to be estimated within a time segment. An update scheme is proposed to refine the whole state at the end of the time segment. Moreover, the relative landmark representation which results in a small cross‐correlation between active state and inactive state is employed to reduce the errors. As a result, both efficiency and robustness can be achieved. Simulations reveal that the results obtained from the proposed compressed H∞ filter, which has lower computational complexity, are very close to those from the full order H∞ filter. Further, the compressed H∞ filter is more robust than EKFs and FastSLAM.

Dyna-QUF: Dyna-Q based univector field navigation for autonomous mobile robots in unknown environments

A novel approach was presented to solve the navigation problem of autonomous mobile robots in unknown environments with dense obstacles based on a univector field method. In an obstacle-free environment, a robot is ensured to reach the goal position with the desired posture by following the univector field. Contrariwise, the univector field cannot guarantee that the robot will avoid obstacles in environments. In order to create an intelligent mobile robot being able to perform the obstacle avoidance task while following the univector field, Dyna-Q algorithm is developed to train the robot in learning moving directions to attain a collision-free path for its navigation. Simulations on the computer as well as experiments on the real world prove that the proposed algorithm is efficient for training the robot in reaching the goal position with the desired final orientation.

Automatic navigation of mobile robots in unknown environments

Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32�% of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work.