Autonomous Exploring System Based on Ultrasonic Sensory Information

An autonomous exploring system for a mobile robot is presented in this article. The system consists of an ultrasonic range sensor (URS) module and a novel method for building a map from exploration of an environment. Instead of random exploration, the proposed approach provides a systematic and efficient strategy to build the map by means of some preferential points. Taking a multitude of observations or measurements by sonar sensors, a mobile robot derives a virtual polygonal map from a set of regressed segments, partial prior known environmental information, and some inference rules for vertices. Additionally, the concept of safe zones is also introduced in the system to keep the mobile robot safe during exploration. Based on the identified virtual map, a searching method is used to select a next best observation to collect the most sufficient information. Several experiments are given to demonstrate the performance of this proposed approach.     

基于视觉FastSLAM的移动机器人自主探索方法*

针对室内环境下机器人的移动和定位需要,提出基于视觉FastSLAM的移动机器人自主探索方法.该方法综合考虑信息增益和路径距离,基于边界选取探索位置并规划路径,最大化机器人的自主探索效率,确保探索任务的完整实现.在FastSLAM 2.0的基础上,利用视觉作为观测手段,有效融合全景扫描和地标跟踪方法,提高数据观测效率,并且引入地标视觉特征增强数据关联估计,完成定位和地图绘制.实验表明,文中方法能正确选取最优探索位置并合理规划路径,完成探索任务,并且定位精度和地图绘制精度较高,鲁棒性较好.     

A Mobile Robot Exploration Strategy with Low Cost Sonar and Tungsten-Halogen Structured Light

Autonomous environment mapping is an essential part of efficiently carrying out complex missions in unknown indoor environments. In this paper, a low cost mapping system composed of a web camera with structured light and sonar sensors is presented. We propose a novel exploration strategy based on the frontier concept using the low cost mapping system. Based on the complementary characteristics of a web camera with structured light and sonar sensors, two different sensors are fused to make a mobile robot explore an unknown environment with efficient mapping. Sonar sensors are used to roughly find obstacles, and the structured light vision system is used to increase the occupancy probability of obstacles or walls detected by sonar sensors. To overcome the inaccuracy of the frontier-based exploration, we propose an exploration strategy that would both define obstacles and reveal new regions using the mapping system. Since the processing cost of the vision module is high, we resolve the vision sensing placement problem to minimize the number of vision sensing in analyzing the geometry of the proposed sonar and vision probability models. Through simulations and indoor experiments, the efficiency of the proposed exploration strategy is proved and compared to other exploration strategies.      

P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction

Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar environment/structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can use the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save the exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be used to speed up the SLAM process and build a more accurate map. We have also derived the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM on a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in indoor environments     

Discovering and restoring changes in object positions using an autonomous robot with laser rangefinders

This paper describes an object rearrangement system for an autonomous mobile robot. The objective of the robot is to autonomously explore and learn about an environment, to detect changes in the environment on a later visit after object disturbances and finally, to move objects back to their original positions. In the implementation, it is assumed that the robot does not have any prior knowledge of the environment and the positions of the objects. The system exploits Simultaneous Localisation and Mapping (SLAM) and autonomous exploration techniques to achieve the task. These techniques allow the robot to perform localisation and mapping which is required to perform the object rearrangement task autonomously. The system includes an arrangement change detector, object tracking and map update that work with a Polar Scan Match (PSM) Extended Kalman Filter (EKF) SLAM system. In addition, a path planning technique for dragging and pushing an object is also presented in this paper. Experimental results of the integrated approach are shown to demonstrate that the proposed approach provides real-time autonomous object rearrangements by a mobile robot in an initially unknown real environment. Experiments also show the limits of the system by investigating failure modes.     

Mapping and exploration in a hierarchical heterogeneous multi-robot system using limited capability robots

This paper focusses on the development of a customised mapping and exploration task for a heterogeneous ensemble of mobile robots. Many robots in the team may have limited processing and sensing abilities. This means that each robot may not be able to execute all components of the mapping and exploration task. A hierarchical system is proposed that consists of computationally powerful robots (managers) at the upper level and limited capability robots (workers) at the lower levels. This enables resources (such as processing) to be shared and tasks to be abstracted. The global environment containing scattered obstacles is divided into local environments by the managers for the workers to explore. Worker robots can be assigned planner and/or explorer tasks and are only made aware of information relevant to their assigned tasks.     

State-chain sequential feedback reinforcement learning for path planning of autonomous mobile robots

This paper deals with a new approach based on Q-learning for solving the problem of mobile robot path planning in complex unknown static environments.As a computational approach to learning through interaction with the environment,reinforcement learning algorithms have been widely used for intelligent robot control,especially in the field of autonomous mobile robots.However,the learning process is slow and cumbersome.For practical applications,rapid rates of convergence are required.Aiming at the problem of slow convergence and long learning time for Q-learning based mobile robot path planning,a state-chain sequential feedback Q-learning algorithm is proposed for quickly searching for the optimal path of mobile robots in complex unknown static environments.The state chain is built during the searching process.After one action is chosen and the reward is received,the Q-values of the state-action pairs on the previously built state chain are sequentially updated with one-step Q-learning.With the increasing number of Q-values updated after one action,the number of actual steps for convergence decreases and thus,the learning time decreases,where a step is a state transition.Extensive simulations validate the efficiency of the newly proposed approach for mobile robot path planning in complex environments.The results show that the new approach has a high convergence speed and that the robot can find the collision-free optimal path in complex unknown static environments with much shorter time,compared with the one-step Q-learning algorithm and the Q(λ)-learning algorithm.     

Active SLAM and Exploration with Particle Filters Using Kullback-Leibler Divergence

Autonomous exploration under uncertain robot location requires the robot to use active strategies to trade-off between the contrasting tasks of exploring the unknown scenario and satisfying given constraints on the admissible uncertainty in map estimation. The corresponding problem, namely active SLAM (Simultaneous Localization and Mapping) and exploration, has received a large attention from the robotic community for its relevance in mobile robotics applications. In this work we tackle the problem of active SLAM and exploration with Rao-Blackwellized Particle Filters. We propose an application of Kullback-Leibler divergence for the purpose of evaluating the particle-based SLAM posterior approximation. This metric is then applied in the definition of the expected information from a policy, which allows the robot to autonomously decide between exploration and place revisiting actions (i.e., loop closing). Extensive tests are performed in typical indoor and office environments and on well-known benchmarking scenarios belonging to SLAM literature, with the purpose of comparing the proposed approach with the state-of-the-art techniques and to evaluate the maturity of truly autonomous navigation systems based on particle filtering.     

Fuzzy Logic Based Behavior Fusion for Navigation of an Intelligent Mobile Robot

This paper presents a new method for behavior fusion control of a mobile robot in uncertain environments.Using behavior fusion by fuzzy logic,a mobile robot is able to directly execute its motion according to range information about environments,acquired by ultrasonic sensors,without the need for trajectory planning.Based on low-level behavior control,an efficient strategy for integrating high-level global planning for robot motion can be formulated,since,in most applications,some information on environments is prior knowledge.A global planner,therefore,only to generate some subgoal positions rather than exact geometric paths.Because such subgoals can be easily removed from or added into the plannes,this strategy reduces computational time for global planning and is flexible for replanning in dynamic environments.Simulation results demonstrate that the proposed strategy can be applied to robot motion in complex and dynamic environments.     

Feature extraction for outdoor mobile robot navigation based on a modified Gauss–Newton optimization approach

This paper discusses the problem of feature detection for semi-structured outdoor environments such as campuses and parks using laser range sensors. In these environments, commonly encountered natural features that can be very useful for mobile robot navigation include edges (large discontinuity) and circles (e.g., trees, pillars). The term feature is used to denote objects which are “likely” to be detectable when the sensor is moved to new locations. Note that there has been no systematic approach for feature detection in outdoor environments. In this paper, we present an algorithm for feature detection. The algorithm consists of data segmentation and parameter acquisition. A modified Gauss–Newton method is proposed for fitting circle parameters iteratively. Experimental results show that the proposed algorithm is efficient in detecting features for semi-structured outdoor environments and is applicable to real time simultaneous localization and mapping.     

A fault-tolerant approach to robot teams

As the applications of mobile robotics evolve it has become increasingly less practical for researchers to design custom hardware and control systems for each problem. This paper presents a new approach to control system design in order to look beyond end-of-lifecycle performance, and consider control system structure, flexibility, and extensibility. Towards these ends the Control ad libitum philosophy was proposed, stating that to make significant progress in the real-world application of mobile robot teams the control system must be structured such that teams can be formed in real-time from diverse components. The Control ad libitum philosophy was applied to the design of the HAA (Host, Avatar, Agent) architecture: a modular hierarchical framework built with provably correct distributed algorithms. A control system for mapping, exploration, and foraging was developed using the HAA architecture and evaluated in three experiments. First, the basic functionality of the HAA architecture was studied, specifically the ability to: (a) dynamically form the control system, (b) dynamically form the robot team, (c) dynamically form the processing network, and (d) handle heterogeneous teams and allocate robots between tasks based on their capabilities. Secondly, the control system was tested with different rates of software failure and was able to successfully complete its tasks even when each module was set to fail every 0.5–1.5 min. Thirdly, the control system was subjected to concurrent software and hardware failures, and was still able to complete a foraging task in a 216 m² environment.     

ARDEA—An MAV with skills for future planetary missions

We introduce a prototype flying platform for planetary exploration: autonomous robot design for extraterrestrial applications (ARDEA). Communication with unmanned missions beyond Earth orbit suffers from time delay, thus a key criterion for robotic exploration is a robot's ability to perform tasks without human intervention. For autonomous operation, all computations should be done on‐board and Global Navigation Satellite System (GNSS) should not be relied on for navigation purposes. Given these objectives ARDEA is equipped with two pairs of wide‐angle stereo cameras and an inertial measurement unit (IMU) for robust visual‐inertial navigation and time‐efficient, omni‐directional 3D mapping. The four cameras cover a $240^{°}$ vertical field of view, enabling the system to operate in confined environments such as caves formed by lava tubes. The captured images are split into several pinhole cameras, which are used for simultaneously running visual odometries. The stereo output is used for simultaneous localization and mapping, 3D map generation and collision‐free motion planning. To operate the vehicle efficiently for a variety of missions, ARDEA's capabilities have been modularized into skills which can be assembled to fulfill a mission's objectives. These skills are defined generically so that they are independent of the robot configuration, making the approach suitable for different heterogeneous robotic teams. The diverse skill set also makes the micro aerial vehicle (MAV) useful for any task where autonomous exploration is needed. For example terrestrial search and rescue missions where visual navigation in GNSS‐denied indoor environments is crucial, such as partially collapsed man‐made structures like buildings or tunnels. We have demonstrated the robustness of our system in indoor and outdoor field tests.     

Motion planning of multiple mobile robots by a combination of learned visibility graphs and virtual impedance

A motion planning algorithm for multiple mobile robots is proposed in this paper. A hierarchical architecture with two layers 'learned visibility graph layer (upper layer)' and 'virtual impedance layer (lower layer)' (one of the potential field planning method) is presented. This system has the following characteristics: (1) is applicable to unknown dynamic environments, (2) is applicable to distributed multiple robot systems and (3) is capable of adequate path generation and motion. At the upper layer, efficient exploration of environments makes it possible to generate sub-shortest paths that avoid static obstacles. At the lower layer, on-line avoidance can be made with virtual impedance against moving obstacles such as other robots. Simulation results show the validity of the proposed method.     

基于动态精简式混合地图的移动机器人自主探索

针对未知环境下移动机器人自主探索和地图创建问题,在机器人操作系统的框架下,提出一种基于动态精简式混合地图的移动机器人自主探索方法.首先,提出一种基于几何规则的候选目标点生成方法,用于快速提取当前的前沿目标点;然后,从信息收益和路径成本的角度,引入一种改进的效用函数来评价候选目标点;最后,利用缓存增量式的原理优化拓扑节点,进而构建精简式混合地图.实验结果表明,通过拓扑图构建策略的改进,所提出方法具有良好的导航性能.     

一种普适机器人系统同时定位、标定与建图方法

机器人定位、传感器网络标定与环境建图是普适机器人系统中三个相互耦合的基本问题, 其有效解决是普适机器人系统提供高效智能服务的前提. 本文提出了普适机器人系统同时机器人定位、传感器网络标定与环境建图的概念, 通过分析三者之间的耦合关系, 给出同时定位、标定与建图问题的联合条件概率表示, 基于贝叶斯公式和马尔科夫特性将其分解为若干可解项, 并借鉴Rao-Blackwellized粒子滤波的思想分别求解. 首先, 联合传感器网络对机器人的观测、机器人对已定位环境特征的观测以及机器人自身控制量,设计了位姿粒子的采样提议分布和权值更新公式; 其次, 联合传感器网络对机器人运动轨迹及已定位环境特征的观测,设计了传感器网络标定的递推公式; 然后, 联合传感器网络和机器人对(已定位或新发现)环境特征的观测,设计了环境建图的递推公式. 给出了完整的同时定位、标定与建图算法, 并通过仿真实验验证了该算法的有效性.     

A global localization approach based on Line-segment Relation Matching technique

This paper presents a novel global localization approach for mobile robots by exploring line-segment features in any structured environment. The main contribution of this paper is an effective data association approach, the Line-segment Relation Matching (LRM) technique, which is based on a generation and exploration of an Interpretation Tree (IT). A new representation of geometric patterns of line-segments is proposed for the first time, which is called as Relation Table. It contains relative geometric positions of every line-segment respect to the others (or itself) in a coordinate-frame independent sense. Based on that, a Relation-Table-constraint is applied to minimize the searching space of IT therefore greatly reducing the processing time of LRM. The Least Square algorithm is further applied to estimate the robot pose using matched line-segment pairs. Then a global localization system can be realized based on our LRM technique integrated with a hypothesis tracking framework which is able to handle pose ambiguity. Sufficient simulations were specially designed and carried out indicating both pluses and minuses of our system compared with former methods. We also presented the practical experiments illustrating that our approach has a high robustness against uncertainties from sensor occlusions and extraneous observation in a highly dynamic environment. Additionally our system was demonstrated to easily deal with initialization and have the ability of quick recovery from a localization failure.     

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

Coordinated multirobot exploration involves autonomous discovering and mapping of the features of initially unknown environments by using multiple robots. Autonomously exploring mobile robots are usually driven, both in selecting locations to visit and in assigning them to robots, by knowledge of the already explored portions of the environment, often represented in a metric map. In the literature, some works addressed the use of semantic knowledge in exploration, which, embedded in a semantic map, associates spatial concepts (like ‘rooms’ and ‘corridors’) with metric entities, showing its effectiveness in improving the total area explored by robots. In this paper, we build on these results and propose a system that exploits semantic information to push robots to explore relevant areas of initially unknown environments, according to a priori information provided by human users. Discovery of relevant areas is significant in some search and rescue settings, in which human rescuers can instruct robots to search for victims in specific areas, for example in cubicles if a disaster happened in an office building during working hours. We propose to speed up the exploration of specific areas by using semantic information both to select locations to visit and to determine the number of robots to allocate to those locations. In this way, for example, more robots could be assigned to a candidate location in a corridor, so the attached rooms can be explored faster. We tested our semantic-based multirobot exploration system within a reliable robot simulator and we evaluated its performance in realistic search and rescue indoor settings with respect to state-of-the-art approaches.     

Development of a real-life EKF based SLAM system for mobile robots employing vision sensing

Developing real-life solutions for implementation of the simultaneous localization and mapping (SLAM) algorithm for mobile robots has been well regarded as a complex problem for quite some time now. Our present work demonstrates a successful real implementation of extended Kalman filter (EKF) based SLAM algorithm for indoor environments, utilizing two web-cam based stereo-vision sensing mechanism. The vision-sensing mechanism is a successful development of a real algorithm for image feature identification in frames grabbed from continuously running videos on two cameras, tracking of these identified features in subsequent frames and incorporation of these landmarks in the map created, utilizing a 3D distance calculation module. The system has been successfully test-run in laboratory environments where the robot is commanded to navigate through some specified waypoints and create a map of its surrounding environment. Our experimentations showed that the estimated positions of the landmarks identified in the map created closely tallies with the actual positions of these landmarks in real-life.     

INTELLIGENT SENSOR FUSION AND LEARNING FOR AUTONOMOUS ROBOT NAVIGATION

ABSTRACT

This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behaviors such as target collecting in the ever-changing physical environment.