Mobile Robot's Map Reconstruction Based on DSmT and Fast-Hough Self-Localization

The obstacle avoidance and navigation are important tasks for a mobile robot in applications such as industry, military, exploration and automated vehicles. This paper presents an implementation of mapping and navigation of autonomous mobile robot using Robot Operating System (ROS) and Orbbec Astra camera. In recent days, Lidar is used for the mapping and navigation problem by sensing the objects which are above floor level by using only a single horizontal scanning line and this may result in inaccurate generation of map leads to collision during autonomous navigation of mobile robots. In order to overcome limitation of Lidar, Orbbec Astra (Kinect) camera is used for mapping of indoor environment which can detect objects which are above and below the floor level and navigation of mobile robot from start to target location without colliding with obstacles. Experimental results show the map of the indoor environment generated using Orbbec Astra camera is matched with real indoor environment, and generated map is tested experimentally to navigate the mobile robot from start to target location without collision with obstacles.KeywordsMobile robotsMappingNavigationOrbbec Astra cameraROS

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

Recently, considerable attention has been focused on the investigation of the use of wireless sensor networks (WSN) to drive mobile robot for efficient exploration in unknown environments. The process of navigation depends heavily on estimating the position of the features provided by sensor within the entire environment. This paper proposes a novel navigation algorithm based on RF wireless sensor networks to simultaneous localization and mapping (SLAM) for an indoor autonomous mobile robot. Triangulation localization method is employed to make the robot locates itself and knows its pose. A simple yet efficient two-step particle filter is applied for map building. Simulation and experimental results show that good localization can be achieved using the proposed method.

Map reconstruction for autonomous mobile robots navigation needs to deal with uncertainties, imprecisions and even imperfections due to the limited sensors quality and knowledge acquisition. An improved fusion machine is proposed by replacing the classical conjunctive operator with T-norm operator in Dezert-Smarandache Theory (DSmT) framework for building grid map using noisy sonar measurements. An experiment using a Pioneer II mobile robot with 16 sonar detectors onboard is done in a small indoor environment, and a 2D Map is built online with our selfdeveloping software platform. A quantitative comparison of the results of our new method for map reconstruction with respect to the classical fusion machine is proposed. We show how the new approach developed in this work outperforms the classical one.

Mobile robot localization and map building are very important for a mobile robot to perform a navigation-based service task to aid the aged or disabled in an indoor environment (office, facilities or at home). In order to enable mobile robot to perform service tasks autonomously, the information of environment in which the mobile robot moves is needed. To obtain the accurate map, many different sensors system and techniques have been developed. Approaches of mapping can roughly be classified according to the kind of sensor data processed and the matching algorithms [S. Thrun et al., 1996, 1998, 2000, 2004; Chong, K.S et al., 1996; C.-C. Wang et al., 2002; G. Dissanayake et al., 2000; B. barshan et al., 1995; Weimin Shen et al., 2005]. In this paper, we proposed a method of map building using interactive GUI for a mobile robot. The reason we proposed this method is that it is difficult for a mobile robot to generate an accurate map although many kinds of sensors are used. The proposed method enables the operator to modify map built by sensors, compared with the real-time video from web camera by using modification tool in interactive GUI. Laser Range Finder (LRF) was used to get the information of the environment in which mobile robot moving. In order to improve self-localization of mobile robot, Extended Kalman Filter (EKF) was introduced. By the results of simulation and experiments, the developed EKF is effective in self-localization of mobile robot. This paper introduces the architecture of the system and gives some experimental results to verify the effectiveness of the developed system. The rest of the paper consists of 5 sections. Section 2 describes the structure of the proposed system. Section 3 presents the developed algorithm of self-localization of mobile robot and gives some simulation and experimental results to verify the effectiveness of the EKF in improving precision of positioning of mobile robot. Section 4 details the developed LRF data processing algorithm. Section 5 introduces the developed interactive GUI. The experimental results are given in Section 6. Section 7 concludes the paper. 13

The generalized Voronoi diagram (GVD) is a powerful environment representation that defines a set of paths at maximal distance from the obstacles. Many works implicitly use this property to define safe navigation strategies for a mobile robot, but, in practice, only a few explicitly extracts in real time the GVD from the robot perception. In this paper, a real-time skeleton-based visual servoing approach is proposed for safe autonomous navigation of mobile robots in unknown structured environments. The control is based on an approximation of the local GVD using the delta medial axis, a fast and robust skeletonization algorithm. The latter produces a filtered skeleton of the freespace surrounding the robot using a pruning parameter that takes into account the robot size. A unified virtual perception frame is also introduced to project the navigable freespace obtained by possibly multiple sensors and observations. After freespace skeletonization and GVD approximation, a visual servoing control is designed and studied to ensure autonomous robot navigation along the extracted GVD branches. The approach is first tested using a simulated environment. Then, an experimental validation is carried out on a real differential wheelchair allowing the system to navigate safely and autonomously without any human intervention. Static and dynamic environment scenarios are studied. The method is immediately generalizable to any differential drive wheeled robot.

Mobile robot navigation with the combination of supervised learning in cerebellum and reward-based learning in basal ganglia

Autonomous exploration and mapping is an important part of autonomous navigation of mobile robots in an unknown environment. This paper proposes a new 3D exploration strategy based on the wavefront algorithm. RGB-D camera is used to obtain environmental information. The diffusion process of wavefront algorithm is used to find frontier points. Firstly, selection function determines the next frontier point to be explored. Then, the mobile robot moves to the frontier point according to the path planned by the way of wavefront algorithm. When the mobile robot reaches the frontier point and completes the mapping, the mobile robot continues to search for the next frontier point. Finally, the exploration strategy is tested using the Robot Operating System (ROS). Simulational experiment shows that the 3D exploration based on the wavefront algorithm can choose the right frontier point and complete the exploration task quickly.

International audience

Robot navigation and obstacle avoidance are from the most important problems in mobile robots, especially in unknown environments. The autonomous navigation of mobile robots has attracted a number of researchers over the years. A wide variety of approaches and algorithms have been proposed to tackle this complex problem. There are many successful localization methods that can determine the robot's position relative to a map using sonar, laser and camera data; however, most localization methods fail under common environmental conditions. In this paper we present a novel method for navigation of autonomous mobile robots in several indoor environments that optimize our old navigation approach, using fuzzy Kalman filter based on extracted lines and fusing data from laser scanner sensors. In this approach, the sonar data and laser measurement are combined using the fuzzy Kalman filter. Finally, the simulation results show that the proposed algorithm offers advantages over previous methods and has improved performance.

The objective of this research is to develop a real time obstacle detection and obstacle avoidance for autonomous navigation of mobile robots using a stereo camera in an unstructured environment. Autonomous navigation of mobile robots demands a) Exact determination of position and orientation of Robot and b) Accurate determination of size, shape, depth and range of potential obstacles in the environment. Simple kinematic model is used for mobile robot and stereo camera with pan and tilt provision is considered for long range operation. Complete 3D reconstruction of object/obstacle is obtained from the stereo matching algorithm and with triangulation method. The pose (position and orientation) of mobile robot is formulated from the static object observation with stereo reference matching points using RANSAC (RANdom SAmple Consensus) in successive frames. Potential field based obstacle avoidance formulation is carried out by using the obstacle range, size information, mobile robot position and orientation. Finally, proportional derivative navigation control loop along with obstacle avoidance algorithm is formulated and verified.

This paper proposes an end-to-end method for training convolutional neural networks for autonomous navigation of a mobile robot. Traditional approach for robot navigation consists of three steps. The first step is extracting visual features from the scene using the camera input. The second step is to figure out the current position by using a classifier on the extracted visual features. The last step is making a rule for moving the direction manually or training a model to handle the direction. In contrast to the traditional multi-step method, the proposed visuo-motor navigation system can directly output the linear and angular velocities of the robot from an input image in a single step. The trained model gives wheel velocities for navigation as outputs in real-time making it possible to be implanted on mobile robots such as robotic vacuum cleaner. The experimental results show an average linear velocity error of 2.2 cm/s and average angular velocity error of 3.03 degree/s. The robot deployed with the proposed model can navigate in a real-world environment by only using the camera without relying on any other sensors such as LiDAR, Radar, IR, GPS, IMU.

This paper presents an improvement for the software implementation (MOFS) of a user adaptive fuzzy control system for autonomous navigation of mobile robots in unknown environments. This improvement consists of a priority areas deflnition where the environment is measured by a PLS laser sensor, in order to get a reduction in the number of fuzzy rules and also in the computational cost, and hence obtaining improvements in the trajectory. This system has been tested in a pioneer mobile robot and on a robotic wheelchair, odometry sensors are used to localize the robots and the goal positions. The system is able to drive the robots to their goal position avoiding static and dynamic obstacles, without using any pre-built map. This approach improves the way to measure the danger of the obstacles, the way to follow the walls of corridors and the detection of doors. These improvements reduce the zigzag efiect of the previous system by making the trajectories signiflcantly straighter and hence reducing the time to reach the goal position.

Development of collision free path planning algorithm for warehouse mobile robot

Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment. Reinforcement Learning methods enable a state action function in mobile robots suited to their environment. During trial-and-error interaction with its surroundings, it helps a robot to find an ideal behavior on its own. The Deep Q Network (DQN) algorithm is used in TurtleBot 3 (TB3) to achieve the goal by successfully avoiding the obstacles. But it requires a large number of training iterations. This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field (APF) algorithms. First, a TB3 Waffle Pi DQN is built and trained to reach the goal. Then the APF shortest path algorithm is incorporated into the DQN algorithm. The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System (ROS). The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algorithm. The performance improvement rate of the proposed DQN + APF in comparison with DQN in terms of the number of successful targets is attained by 88%. The performance of the proposed DQN + APF in comparison with DQN in terms of average time is achieved by 0.331 s. The performance of the proposed DQN + APF in comparison with DQN average rewards in which the positive goal is attained by 85% and the negative goal is attained by −90%.