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.      

Effective Maximum Likelihood Grid Map With Conflict Evaluation Filter Using Sonar Sensors

In this paper, we address the problem of building a grid map using cheap sonar sensors, i.e., the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, which is called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood (ML) approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors and does not require adjustment of parameters for various environments.      

Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements

In this paper, we address the problem of building a grid map as accurately as possible using inexpensive and error-prone sonar sensors. In this research area, incorrect sonar measurements, which fail to detect the nearest obstacle in their beamwidth, generally have been dealt with in the same manner as correct measurements or have been excluded from the mapping. In the former case, the map quality may be severely degraded. In the latter case, the resulting map may have insufficient information after the incorrect measurements are removed because only correct measurements are frequently insufficient to cover the whole environment. We propose an efficient grid-mapping approach that incorporates incorrect measurements in a specialized manner to build a better map; we call this the enhanced maximum likelihood (eML) approach. The eML approach fuses the correct and incorrect measurements into a map based on sub-maps generated from each set of measurements. We also propose the maximal sound pressure (mSP) method to detect incorrect sonar readings using the sound pressure of the waves from sonar sensors. In several indoor experiments, integrating the eML approach with the mSP method achieved the best results in terms of map quality among various mapping approaches. We call this the maximum likelihood based on sub-maps (MLS) approach. The MLS map created using only two sonar sensors exhibited similar accuracy to the reference map, which was an accurate representation of the environment.     

Autonomous topological modeling of a home environment and topological localization using a sonar grid map

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.     

Sonar Sensor-Based Efficient Exploration Method Using Sonar Salient Features and Several Gains

This paper describes a sonar sensor-based exploration method. To build an accurate map in an unknown environment during exploration, a simultaneous localization and mapping problem must be solved. Therefore, a new type of sonar feature called a ??sonar salient feature?? (SS-feature), is proposed for robust data association. The key concept of an SS-feature is to extract circle feature clouds on salient convex objects from environments by associating sets of sonar data. The SS-feature is used as an observation in the extended Kalman filter (EKF)-based SLAM framework. A suitable strategy is needed to efficiently explore the environment. We used utilities of driving cost, expected information about an unknown area, and localization quality. Through this strategy, the exploration method can greatly reduce behavior that leads a robot to explore a previously visited place, and thus shorten the exploration distance. A robot can select a favorable path for localization by localization gain during exploration. Thus, the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, and it ensures that a robot can build an accurate map fully autonomously with sonar sensors in various home environments.     

Supervised semantic labeling of places using information extracted from sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. As an example, natural language terms like “corridor” or “room” can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with a relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments.     

Topological localization with kidnap recovery using sonar grid map matching in a home environment

This paper presents a method of topological localization with kidnap recovery capability in a home environment using only low-cost sonar sensors. The proposed method considers both pose tracking and relocation problems. The pose tracking is achieved by calculating node probability using grid-map matching and relative motion model. The relocation method detects the kidnap automatically and recovers it using multiple hypothesis tracking. After kidnap recovery, it also provides a criterion for selecting a reasonable hypothesis for returning to the pose tracking stage autonomously. Experimental results in a real home environment verify that the proposed localization method provides a reliable and convergent node probability when the robot is kidnapped.     

Intelligent and Efficient Strategy for Unstructured Environment Sensing Using Mobile Robot Agents

In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulation results show the effectiveness of this algorithm.     

Deformable Motion: Squeezing into Cluttered Environments

We present an interactive method that allows animated characters to navigate through cluttered environments. Our characters are equipped with a variety of motion skills to clear obstacles, narrow passages, and highly constrained environment features. Our control method incorporates a behavior model into well‐known, standard path planning algorithms. Our behavior model, called deformable motion, consists of a graph of motion capture fragments. The key idea of our approach is to add flexibility on motion fragments such that we can situate them into a cluttered environment via constraint‐based formulation. We demonstrate our deformable motion for realtime interactive navigation and global path planning in highly constrained virtual environments.     

Exploration of 2D and 3D Environments using Voronoi Transform and Fast Marching Method

Robot navigation in unknown environments requires an efficient exploration method. Exploration involves not only to determine towards the robot must to move but also motion planning, and simultaneous localization and mapping processes. The final goal of the exploration task is to build a map of the environment that previously the robot didn’t know. This work proposes the Voronoi Fast Marching method, that uses a Fast Marching technique on the Logarithm of the Extended Voronoi Transform of the environment’s image provided by sensors, to determine a motion plan. The Logarithm of the Extended Voronoi Transform imitates the repulsive electric potential from walls and obstacles, and the Fast Marching Method propagates a wave over that potential map. The trajectory is calculated by the gradient method. The robot is directed towards the most unexplored and free zones of the environment so as to be able to explore all the workspace. Finally, to build the environment map while the robot is carrying out the exploration task, a SLAM (Simultaneous Localization and Modelling)algorithm is implemented, the Evolutive Localization Filter (ELF) based on a differential evolution technique. The combination of these methods provide a new autonomous exploration strategy to construct consistent maps of 2D and 3D indoor environments.     

A nonparametric learning approach to range sensing from omnidirectional vision

We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available during a training phase. Our model not only yields the most likely distance to obstacles in all directions, but also the predictive uncertainties for these estimates. This information can be utilized by a mobile robot to build an occupancy grid map of the environment or to avoid obstacles during exploration—tasks that typically require dedicated proximity sensors such as laser range finders or sonars. We show in this paper how an omnidirectional camera can be used as an alternative to such range sensors. As the learning engine, we apply Gaussian processes, a nonparametric approach to function regression, as well as a recently developed extension for dealing with input-dependent noise. In practical experiments carried out in different indoor environments with a mobile robot equipped with an omnidirectional camera system, we demonstrate that our system is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.     

移动机器人非视觉传感器及其信号处理方法

非视觉传感器是机器人认识和了解外部环境的重要途径，移动机器人常用的非视觉 传感器包括超声、红外、接近传感器等．这些传感器大多是以环或阵列的形式出现，因此其 信号处理往往要占用机器人大量的CPU时间．本文提出了一种采用多DSP控制和处理各类非视 觉传感器的方法，给出了传感器信号处理的原理和具体实现．同时我们引入了并行处理的机 制，各类传感器信号处理可同时进行，在很大程度上提高了机器人传感器信号处理的速度， 有利于机器人在实时动态环境中运行．并给出了非视觉传感器信号处理的实验结果，验证了 该方法的有效性．      

Randomized path planning for tasks requiring the release and regrasp of objects

This paper presents a randomized planning algorithm for manipulation tasks that require the robot to release and regrasp an object in different robot postures. Such problems arise, for example, in robotic suturing and knot tying, and in assembly tasks where parts must be guided through complex environments. Formulating the problem as one of planning on a foliated manifold, we present a randomized planning algorithm that, unlike existing methods, involves sampling and tree propagation primarily in the task space manifold; such an approach significantly improves computational efficiency by reducing the number of projections to the constraint manifold, without incurring any significant increases in the number of release-regrasp sequences. We also propose a post-processing topological exploration algorithm and path refinement procedure for reducing the number of release-regrasp sequences in a solution path, independent of the algorithm used to generate the path. Experiments involving spatial open chains with up to 10 degrees of freedom, operating in complex obstacle-filled environments, show that our algorithm considerably outperforms existing algorithms in terms of computation time, path length, and the number of release-regrasp operations.     

Multi-robot olfactory search in structured environments

This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots. While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for the exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations.     

Heterogeneous multisensor fusion for mapping dynamic environments

In this paper, we propose a heterogeneous multisensor fusion algorithm for mapping in dynamic environments. The algorithm synergistically integrates the information obtained from an uncalibrated camera and sonar sensors to facilitate mapping and tracking. The sonar data is mainly used to build a weighted line-based map via the fuzzy clustering technique. The line weight, with confidence corresponding to the moving object, is determined by both sonar and vision data. The motion tracking is primarily accomplished by vision data using particle filtering and the sonar vectors originated from moving objects are used to modulate the sample weighting. A fuzzy system is implemented to fuse the two sensor data features. Additionally, in order to build a consistent global map and maintain reliable tracking of moving objects, the well-known extended Kalman filter is applied to estimate the states of robot pose and map features. Thus, more robust performance in mapping as well as tracking are achieved. The empirical results carried out on the Pioneer 2DX mobile robot demonstrate that the proposed algorithm outperforms the methods a using homogeneous sensor, in mapping as well as tracking behaviors.     

Real-time map building and navigation for autonomous robots inunknown environments

An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.     

A real-time 3D motion planning and simulation scheme for nonholonomic systems

We propose a new motion planning and simulation scheme for nonholonomic systems in this paper to provide a practical solution for these application problems taking into account of real-time obstacle avoidance and the continuous curvature path generation simultaneously in 3D unknown environment. The proposed motion planning and simulation scheme generates the motion path using a new universal Euler spiral generation algorithm, which is locally optimal based on perceived points of view. The generated Euler spiral solution can be non-symmetrical and easily implemented while maintaining a C² continuous. It is therefore more flexible and powerful in dealing with dynamic situations in real-time, compared with current symmetrical Euler spirals solutions. Real-time solutions are particularly important in navigation in unknown environments. The universal Euler spiral algorithm proposed displays a smaller maximum curvature value and smaller mean square curvature value than the conventional symmetrical algorithm in tested cases. Another significant contribution of our work is the new motion planning scheme which extend current 2D based motion planning into three-dimensional (3D) space. In this paper, we have conducted experiments and describe simulation results including 3D motion trajectory modeling for a flight simulation.     

Path planning directed motion control of virtual humans in complex environments

Natural motion synthesis of virtual humans have been studied extensively, however, motion control of virtual characters actively responding to complex dynamic environments is still a challenging task in computer animation. It is a labor and cost intensive animator-driven work to create realistic human motions of character animations in a dynamically varying environment in movies, television and video games. To solve this problem, in this paper we propose a novel approach of motion synthesis that applies the optimal path planning to direct motion synthesis for generating realistic character motions in response to complex dynamic environment. In our framework, SIPP (Safe Interval Path Planning) search is implemented to plan a globally optimal path in complex dynamic environments. Three types of control anchors to motion synthesis are for the first time defined and extracted on the obtained planning path, including turning anchors, height anchors and time anchors. Directed by these control anchors, highly interactive motions of virtual character are synthesized by motion field which produces a wide variety of natural motions and has high control agility to handle complex dynamic environments. Experimental results have proven that our framework is capable of synthesizing motions of virtual humans naturally adapted to the complex dynamic environments which guarantee both the optimal path and the realistic motion simultaneously.     

Robust RBPF-SLAM for Indoor Mobile Robots Using Sonar Sensors in Non-Static Environments

We propose a robust simultaneous localization and mapping (SLAM) with a Rao-Blackwellized particle filter (RBPF) algorithm for mobile robots using sonar sensors in non-static environments. The algorithm consists of three parts: sampling from multiple ancestor sets, estimating intermediate paths for map updates and eliminating spurious landmarks using negative information from sonar sensors. The proposed sampling method, in which particles are sampled from multiple ancestor sets, increases the robustness of the estimation of the robot's pose, even if environmental changes corrupt observations. This step increases the probability of some particles being sampled from correct ancestor sets that are updated by observations reflected from stationary objects. When particles are sampled from several time steps earlier, however, observations at intermediate time steps cannot be used to update the map because of the lack of information about the intermediate path. To update the map with all sensor information, the intermediate path is estimated after particles are sampled from ancestor sets. Finally, spurious landmarks still exist on the map representing objects that were eliminated or that were extracted by error in cluttered areas. These are eliminated in the final step using negative information from the sonar sensors. The performance of the proposed SLAM algorithm was verified through simulations and experiments in various non-static environments.     

A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot

We address the problem of online path planning for optimal sensing with a mobile robot. The objective of the robot is to learn the most about its pose and the environment given time constraints. We use a POMDP with a utility function that depends on the belief state to model the finite horizon planning problem. We replan as the robot progresses throughout the environment. The POMDP is high-dimensional, continuous, non-differentiable, nonlinear, non-Gaussian and must be solved in real-time. Most existing techniques for stochastic planning and reinforcement learning are therefore inapplicable. To solve this extremely complex problem, we propose a Bayesian optimization method that dynamically trades off exploration (minimizing uncertainty in unknown parts of the policy space) and exploitation (capitalizing on the current best solution). We demonstrate our approach with a visually-guide mobile robot. The solution proposed here is also applicable to other closely-related domains, including active vision, sequential experimental design, dynamic sensing and calibration with mobile sensors.