Semi-structured Environment Research Articles

Autonomous Navigation of a Surveillance Robot in Harsh Outdoor Road Environments

This paper deals with the autonomous navigation problem of a mobile robot in outdoor road environments. The target application is surveillance in petroleum storage bases. Although there have been remarkable technological achievements recently in the area of outdoor navigation, robotic systems are still expensive due to a large number of high cost sensors. This paper proposes the reliable extraction algorithm of traversable regions using a single onboard Laser Range Finder (LRF) in outdoor road environments. The traversable regions are derived from the classifications of the road surfaces, curbs, and obstacles. The proposed scheme was experimentally tested in success. Since there are many potential applications that require autonomous service robots to move in semistructured road environments, the proposed scheme can be widely used as a low-cost practical solution.

Optimal docking pose and tactile hook-localisation strategy for AUV intervention: The DIFIS deployment case

The DIFIS project has proposed a new solution for the immediate intervention directly on tanker wrecks so as to contain any leakages and prevent eventual pollution. The method could be extended also to oil well-blow-out cases such as the recent accident in the Gulf of Mexico. The DIFIS deployment typically requires the use of ROVs and dedicated dynamic-positioning ships that increase the cost significantly and make the operations weather-dependent. Eventual AUV use would result in much more efficient and flexible deployment procedures. The scenario studied here consists of a hook-grasping task that is part of the DIFIS mooring procedure. The overall objective is to automate certain processes enabling the use of AUVs or, at least, enhancing the currently foreseen ROV operations. A two-step method is presented consisting of a genetic algorithm for the determination of the optimum docking pose for the vehicle, and a particle filter algorithm that runs on a later stage for the tactile localisation of the hook. The method proposed is rather generic and can be extended to several steps of the DIFIS Deployment procedure, or even to other AUV intervention missions in a semi-structured environment. Results from the two algorithms are also presented and discussed.

A Multi-Sensorial Hybrid Control for Robotic Manipulation in Human-Robot Workspaces

Autonomous manipulation in semi-structured environments where human operators can interact is an increasingly common task in robotic applications. This paper describes an intelligent multi-sensorial approach that solves this issue by providing a multi-robotic platform with a high degree of autonomy and the capability to perform complex tasks. The proposed sensorial system is composed of a hybrid visual servo control to efficiently guide the robot towards the object to be manipulated, an inertial motion capture system and an indoor localization system to avoid possible collisions between human operators and robots working in the same workspace, and a tactile sensor algorithm to correctly manipulate the object. The proposed controller employs the whole multi-sensorial system and combines the measurements of each one of the used sensors during two different phases considered in the robot task: a first phase where the robot approaches the object to be grasped, and a second phase of manipulation of the object. In both phases, the unexpected presence of humans is taken into account. This paper also presents the successful results obtained in several experimental setups which verify the validity of the proposed approach.

Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation

This paper describes a new model for understanding natural language commands given to autonomous systems that perform navigation and mobile manipulation in semi-structured environments. Previous approaches have used models with fixed structure to infer the likelihood of a sequence of actions given the environment and the command. In contrast, our framework, called Generalized Grounding Graphs, dynamically instantiates a probabilistic graphical model for a particular natural language command according to the command's hierarchical and compositional semantic structure. Our system performs inference in the model to successfully find and execute plans corresponding to natural language commands such as "Put the tire pallet on the truck." The model is trained using a corpus of commands collected using crowdsourcing. We pair each command with robot actions and use the corpus to learn the parameters of the model. We evaluate the robot's performance by inferring plans from natural language commands, executing each plan in a realistic robot simulator, and asking users to evaluate the system's performance. We demonstrate that our system can successfully follow many natural language commands from the corpus.

A handy new design paradigm

Abstract. In light of technological advances, researchers have lost sight of robotic grippers/end effectors design intent. In a semi-structured environment the biomimetic approach is impractical due to the high complexity of the mechanism and control algorithms. Current industrial grippers are robust, but lack the flexibility that allows for in hand manipulation. The authors believe that underactuated grippers provide the best approach to allow for in hand manipulation along with being rugged enough for an industrial setting. Thinking of the robotic gripper and the robotic arm as one system (as opposed to two separate subsystems), one is capable of using the degrees of freedom of the robot in conjunction with that of the gripper to provide the desired motion profile without the complexity of running two subsystems. This paper will outline where recent grippers have failed and will introduce a new design paradigm for grippers along with several underactuated gripper ideas. This paper was presented at the IFToMM/ASME International Workshop on Underactuated Grasping (UG2010), 19 August 2010, Montréal, Canada.

Cooperation in distributed surveillance systems for dense regions

In this paper, we envision a distributed surveillance system where spatially distributed sensors of different modalities can sense collaboratively and continuously large-scale, dynamic, dense and semi-structured environments. The paper discusses surveillance operations in complex and dense environments, such as littoral regions, and describes the key features of distributed surveillance systems. Effective and robust cooperation among the artificial and/or the human agents can synergistically improve the performance of these systems and can endow them with higher-level faculties, such as cooperative target detection and tracking. Different forms of cooperative activities in distributed surveillance systems are mentioned. The paper focuses on intra and inter-platform target cueing and handoff as augmentative forms of cooperation in distributed surveillance.

Perceived Insomnia, Anxiety, and Depression among Older Russian Immigrants

There are few data on the relationships of anxiety and depression to insomnia among immigrants in the United States. Observations of high rates of symptoms of these associated conditions among older Russians indicate the need to focus clinical attention on this population. Relationships of self-reported depression and anxiety to insomnia complaints were investigated in a community-based sample of older Russian immigrants. Volunteers (N=307) were urban community-residing Russians (ages 50 to 95 years; 54% women). Surveys were conducted in a semistructured environment by bilingual educators in various community centers. 93% reported a major health problem, 83% experienced pain, and 62% had problems engaging in daily activities. Of the sample, 61% reported an insomnia complaint, and 43% considered depression and/or anxiety to be a major impairment. Logistic regression analysis showed that insomnia was the most important predictor of perceived anxiety and depression; the corresponding multivariate-adjusted odds ratio was 4.37. Insomnia complaints and perceptions of depression and anxiety among older Russians may have a synergistic effect. Both patients and primary-care physicians should be better educated regarding the recognition of barriers limiting access to adequate health care among older Russians.

Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments

We describe a practical path-planning algorithm for an autonomous vehicle operating in an unknown semi-structured (or unstructured) environment, where obstacles are detected online by the robot’s sensors. This work was motivated by and experimentally validated in the 2007 DARPA Urban Challenge, where robotic vehicles had to autonomously navigate parking lots. The core of our approach to path planning consists of two phases. The first phase uses a variant of A* search (applied to the 3D kinematic state space of the vehicle) to obtain a kinematically feasible trajectory. The second phase then improves the quality of the solution via numeric non-linear optimization, leading to a local (and frequently global) optimum. Further, we extend our algorithm to use prior topological knowledge of the environment to guide path planning, leading to faster search and final trajectories better suited to the structure of the environment. We present experimental results from the DARPA Urban Challenge, where our robot demonstrated near-flawless performance in complex general path-planning tasks such as navigating parking lots and executing U-turns on blocked roads. We also present results on autonomous navigation of real parking lots. In those latter tasks, which are significantly more complex than the ones in the DARPA Urban Challenge, the time of a full replanning cycle of our planner is in the range of 50—300 ms.

An approach to avoid obstacles in mobile robot navigation: the tangential escape

An approach to guide a mobile robot from an initial position to a goal position avoiding any obstacle in its path, when navigating in a semi-structured environment, is proposed in this paper. Such an approach, hereinafter referred to as tangential escape, consists in changing the current robot orientation through a suitable combination of the values of the angular and linear velocities (the control actions) whenever an obstacle is detected close to it. Then, the robot starts navigating in parallel to the tangent to the obstacle, regarding the point of the obstacle boundary the robot sensing system identifies as the closest one. The stability of the control system designed according this approach is proven, showing that the robot reaches any reachable goal, with or without a prescribed final orientation. Such a control system is programmed onboard a mobile platform whose sensing system is a laser scanner which provides 181 range measurements, for experimental validation. The results obtained are presented and discussed, allowing concluding that the tangential escape approach is able to guide the robot along trajectories that result in a reduction of the traveling time, thus saving batteries and reducing the motor wearing.

Telemanipulación y locomoción mediante robots modulares reconfigurables en entornos semiestructurados

This paper is focused on the application of modular robots in semi-structured environments. Manipulation and displacement capabilities are the main advantages of this self-configurable system. Control architecture for modular systems has been specifically designed. Modules are ensemble in order to become more complex mechanisms, called molecules. Molecules work in a similar manner as a common robot and can change their configuration according to task requirements. This system, called RobMAT, includes an advanced interface for teleoperation. It allows guiding modules and molecules, and also calibrating remote environments. Experiments have highlighted the relevance of module synchronization and trajectory control. Synchronization is a critical point for module cooperation, whereas specific trajectory control is required for improving guidance performance

Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation

This paper proposes a geometrical feature detection system which is to be used with conventional 2D laser range finders. It consists of three main modules: data acquisition and pre-processing, segmentation and landmark extraction and characterisation. The novelty of this system is a new approach for laser data segmentation based on an adaptive curvature estimation. Contrary to other works, this approach divides the laser scan into line and curve segments. Then, these items are used to directly extract several types of landmarks associated with real and virtual features of the environment (corners, center of tree-like objects, line segments and edges). For each landmark, characterisation provides not only the parameter vector, but also complete statistical information, suitable to be used in a localization and mapping algorithm. Experimental results show that the proposed approach is efficient to detect landmarks for structured and semi-structured environments.

</title> </titles> <publication_date> <month>06</month> <year>2007</year> </publication_date> <pages> <first_page></first_page> <last_page></last_page> </pages> <publisher_item> <item_number item_number_type='arNumber'>4284261</item_number> </publisher_item> <doi_data> <doi>10.1109/MIM.2007.4284261</doi> <resource>http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4284261</resource> </doi_data> </journal_article> <journal_article> <titles> <title><![CDATA[Autonomous Navigation: Achievements in Complex Environments

During the past decade, an explosion of interest in the estimation of an autonomous robot's location state and that of its surroundings, known as simultaneous localization and map building (SLAM), is evident. The goal of an autonomous vehicle performing SLAM is to build a map incrementally by using the uncertain information extracted from its sensors, while simultaneously using that map to localize itself with respect to a reference coordinate frame. To demonstrate the state of the art in autonomous navigation, this article focuses on outdoor research work within complex, semi-structured environments with an array of vehicles, using RADAR and laser range finders. Two classes of sensors that we use to get information are proprioceptive sensors and exteroceptive sensors. This paper focuses on sensor data interpretation, information extraction, and data association.

Robot learning through task identification

The operation of an autonomous mobile robot in a semi-structured environment is a complex, usually non-linear and partly unpredictable process. Lacking a theory of robot–environment interaction that allows the design of robot control code based on theoretical analysis, roboticists still have to resort to trial-and-error methods in mobile robotics. The RobotMODIC project aims to develop a theoretical understanding of a robot’s interaction with its environment, and uses system identification techniques to identify the system robot–task–environment. In this paper, we present two practical examples of the RobotMODIC process: mobile robot self-localisation and mobile robot training to achieve door traversal. In both examples, a transparent mathematical function is obtained that maps inputs–sensory perception in both cases–to output — location and steering velocity respectively. Analysis of the obtained models reveals further information about the way in which a task is achieved, the relevance of individual sensors, possible ways of obtaining more parsimonious models, etc.

Conceptual Framework for Quantitative Modeling of Semi-structured MADM

In some situations, MADM matrix is not distinguished completely at the first stage of decision making, because of the complexity of environment. These complexities lead to incomplete cognition and non-optimal decision making. In such ”semi-structured” environment, due to its high degree of complexity, the whole environment is not identifiable for Decision Maker (DM). We design an autonomous agent for semi-structured MADM that solves problems when alternatives have incomplete structure and DM is not able to recognize the whole alternatives of the environment for optimal decision making. The proposed model is a systematic approach for semi-structured MADM with multi-layer mathematical model. The Agent's Stepwise Response Generator (ASRG) moves in semi-structured environment over decision surface step by step to generate hidden alternatives. The new alternatives are designed to go through Feasibility Analyzer and Dynamic Filter Module. The procedure is continued with a closed loop feedback which results in the construction of the Meta-Decision phase.

Human presence detection and tracking for a concierge robot

This paper reports on the development of a social-oriented mobile robot, that is able to reliably navigate in a semi-structured environment and to interact with people in some specific way. The navigation of the robot is achieved using a behaviour-based approach, where properly ordered sequences of actions, turns and transitions bring the robot from place to place. The robot can detect the presence and position of people through its vision system, that has been adapted to detect a special badge carried by the user. The result of this work is a “concierge robot” able to offer practical information to the visitors of a public building.

Simultaneous localization and map-building in outdoors semi-structured environments

This paper addresses the issue of Simultaneous localization and map-building (SLAM) in large scale outdoor environments. A dead-reckoning scheme appropriate also for skid-steered mobile robots is used. A line feature-based map is constructed on-the-fly and assists in a better estimation for the robot pose. The dead-reckoning is based on internal sensors (inertial data and odometry) only. The information from an experimentally derived kinematic model and an onboard Inertial Navigation System (INS) is fused using a simple and fast maximum likelihood formulation to produce estimates for the linear and angular velocity of the robot. The map is constructed using two laser sensors located to cover a full 360° sector around the robot. A second estimate for the robot pose is derived from a map matching algorithm. The two estimates are fused together using a covariance intersection filter. We verify our approach with large scale experiments in outdoors structured environments following paths with steep turns and variable velocity.

Path quality measures for sensor-based motion planning

A mobile robot requires the perception of its local environment for position estimation. Traditional planning methodologies build plans by considering the obstacles position in the environment, and assume that the sensorial system is able to localize the robot. Not all sensorial systems are able to maintain the robot accurately localized at each instant and require a perception planning process to localize the robot at certain points. Recent works of different researchers, tend to overcome the problem of simultaneous perception-action planning. To plan simultaneously perceptual localization activities and movements it is required to determine the robot’s position uncertainty evolution and how does this uncertainty affect the perceptual problem in order to localize the mobile robot. The proposed algorithm provides a methodology to evaluate the uncertainty in an indoor semi-structured environment, which can be used to plan the robot’s movements and localization perception actions, and achieve an integrated plan able to reach the goal. The planning includes the position uncertainty analysis and the probability of success calculation as a path quality measurement. The resultant module has been integrated successfully in a more complex navigation system. Experiments demonstrate the effectiveness of the proposed method in real world applications.

Decentralized autonomous AGV system for material handling

Decentralized control of an autonomous automated guided vehicle system (AGVS) used for material handling is expected to lead to high system flexibility and robustness. A complete control methodology for decentralized autonomous AGVS control was developed and implemented in a computer-integrated manufacturing environment. The methodology addresses all aspects of AGVS functionality: system management, navigation and load transfer. Hierarchical fuzzy behaviour-based control, a reactive navigation scheme, was expanded to multirobot control in semistructured environments by incorporating a priori path optimization and right-of-the-way determination.

Mobile robot localization using a non-linear evolutionary filter

This article describes a localization system for autonomous mobile robot navigation in an indoor semi-structured environment. A peripheral ring of 24 ultrasonic sensors and a camera with a motorized zoom on a pan-tilt platform are used to obtain the information required for the localization process. A non-linear filter based on a genetic algorithm as an emerging optimization method to search for optimal positions is presented. The proposed algorithm is based upon an iterative extended Kalman filter (EKF), which utilizes matches between observed geometric beacons and a generic map of beacon locations and the detection of artificial landmarks, to correct the position and orientation of the vehicle. No exhaustive map of the environment is provided to the mobile robot. It must work with a generic description of the kinds of entities in the environment. The resulting self-localization module has been integrated successfully in a more complex navigation system based on a reactive architecture. Various experimental results show the effectiveness of the presented algorithm, including a comparison with the EKF method.

A Genetic Algorithm for Mobile Robot Localization Using Ultrasonic Sensors

A mobile robot requires the perception of its local environment for position estimation. Ultrasonic range data provide a robust description of the local environment for navigation. This article presents an ultrasonic sensor localization system for autonomous mobile robot navigation in an indoor semi-structured environment. The proposed algorithm is based upon an iterative non-linear filter, which utilizes matches between observed geometric beacons and an a-priori map of beacon locations, to correct the position and orientation of the vehicle. A non-linear filter based on a genetic algorithm as an emerging optimization method to search for optimal positions is described. The resulting self-localization module has been integrated successfully in a more complex navigation system. Experiments demonstrate the effectiveness of the proposed method in real world applications.