Qualitative navigation for mobile robots in indoor environments

This article describes a novel qualitative navigation method for mobile robots in indoor environments. The approach is based on qualitative representations of variations in sensor behavior between adjacent regions in space. These representations are used to localize and guide planning and reaction. Off-line, the system accepts as input a line-based diagram of the environment and generates a map based on a simple qualitative model of sensor behavior. During execution, the robot controller integrates this map into a reaction module. This architecture has been tested both in simulation and on a real mobile robot. Results from both trials are provided.     

A neural framework for adaptive robot control

This paper investigates how dynamics in recurrent neural networks can be used to solve some specific mobile robot problems such as motion control and behavior generation. We have designed an adaptive motion control approach based on a novel recurrent neural network, called Echo state networks. The advantage is that no knowledge about the dynamic model is required, and no synaptic weight changing is needed in presence of time varying parameters in the robot. To generate the robot behavior over time, we adopted a biologically inspired approach called neural fields. Due to its dynamical properties, a neural field produces only one localized peak that indicates the optimum movement direction, which navigates a mobile robot to its goal in an unknown environment without any collisions with static or moving obstacles.     

突破环境限制的导盲方法

目的 目前的导盲机器人大都局限于特定的环境,难以适应环境变换,为使导盲机器人突破空间限制,在多种不同的环境中引导盲人,提出一种基于环境地图创建的导盲方法。方法 首先,根据同步定位与制图算法(SLAM)创建2维环境地图并同步定位;然后,采用A^*启发式算法在已知地图中进行静态全局路径规划,再结合人工势场法在导盲机器人行走过程中进行动态避障;最后,在机器人操作系统(ROS)框架下构建导盲机器人软件控制系统,各功能节点按照特定规则相互通讯,使控制系统更加有序高效。结果 在3种典型的环境下进行实验,结果表明,与其他导盲方法相比,本文方法不再拘泥于单一的空间或特定的环境,适用范围更广也更灵活;此外,导盲精度也更高,地图创建过程中,特征总数达30个时,特征估计误差仅为5~35 cm,行走多达12000步时,自身位置估计误差仅为0~3 m,在路径规划过程中,导盲机器人路径长达100 m时的轨迹误差仅在0.4 m以下;相较而言,此方法更能满足日常导盲需要。结论 本文提出的适用于多种环境的导盲方法,实验结果表明,该方法在多种环境中所创建的地图与实际场景相符,导盲机器人行走轨迹与规划路径基本一致,导盲精度相对较高,并且普遍地适用于视力障碍者日常活动的室内外区域,还能灵活地适应环境的变换,更具实用价值。     

Reactive navigation through multiscroll systems: from theory to real-time implementation

In this paper a new reactive layer for multi-sensory integration applied to robot navigation is proposed. The new robot navigation technique exploits the use of a chaotic system able to be controlled in real-time towards less complex orbits, like periodic orbits or equilibrium points, considered as perceptive orbits. These are subject to real-time modifications on the basis of environment changes acquired through a distributed sensory system. The strategy is inspired to the olfactory bulb neural activity observed in rabbits subject to external stimuli. The mathematical details of the approach are given including simulation results in a virtual environment. Furthermore the proposed strategy has been tested on an experimental environment consisting of an FPGA-based hardware driving an autonomous roving robot. The obtained results demonstrate the capability to perform a real-time navigation control.     

Imagination and situated cognition

Abstract

We describe a novel agent architecture based on the idea that cognition is imagined interaction, i.e. that cognitive tasks are performed by interacting with an imaginary world. We demonstrate the architecture by its application to a subsumption-based mobile robot. The robot's interactive abilities include exploration of an environment and goal-directed navigation within a previously explored environment. Imagination enables the robot to read and make use of maps, allowing it to reason about unfamiliar environments as well.     

Design,development, and control of a tough electrohydraulic hexapod robot for subsea operations

Abstract

In this paper, the design, the development, and the control for an 18 degree-of-freedom electrohydraulic hexapod robot for subsea operations are presented. The hexapod, called HexaTerra, can be equipped with a trenching machine, and move over obstacles and on sloped terrain. Optimization techniques are employed to size the robot legs. Rigid body equations of motion and hydraulic dynamics are developed. Compact electrohydraulic components are sized and selected taking into account the leg kinematics and system dynamic analysis. A model-based control system design is implemented in a real-time environment, able to produce the overall functionality and performance. Experimental results obtained from preliminary tests with the developed electrohydraulic hexapod show good controlled performance and demonstrate excellent system stability over obstacles.     

FUZZY LOGIC STRATEGIES TO CONTROL AN AUTONOMOUS MOBILE ROBOT

ABSTRACT

The present work describes some strategies for allowing an autonomous mobile robot to maneuver in an unknown environment. Such strategies are based in fuzzy control rules that are determined to correspond to the linguistic control rules of human operator's strategies. They are transformed into algorithms to control the steering and the speed of a mobile robot. Perception of the environment is simulated by means of ultrasonic sensors.     

Development of a separable search-and-rescue robot composed of a mobile robot and a snake robot

Abstract

In this study, we propose a new robot system consisting of a mobile robot and a snake robot. The system works not only as a mobile manipulator but also as a multi-agent system by using the snake robot's ability to separate from the mobile robot. Initially, the snake robot is mounted on the mobile robot in the carrying mode. When an operator uses the snake robot as a manipulator, the robot changes to the manipulator mode. The operator can detach the snake robot from the mobile robot and command the snake robot to conduct lateral rolling motions. In this paper, we present the details of our robot and its performance in the World Robot Summit.     

Self-calibration of environmental camera for mobile robot navigation

An environmental camera is a camera embedded in a working environment to provide vision guidance to a mobile robot. In the setup of such robot systems, the relative position and orientation between the mobile robot and the environmental camera are parameters that must unavoidably be calibrated. Traditionally, because the configuration of the robot system is task-driven, these kinds of external parameters of the camera are measured separately and should be measured each time a task is to be performed. In this paper, a method is proposed for the robot system in which calibration of the environmental camera is rendered by the robot system itself on the spot after a system is set up. Specific kinds of motion patterns of the mobile robot, which are called test motions, have been explored for calibration. The calibration approach is based upon executing certain selected test motions on the mobile robot and then using the camera to observe the robot. According to a comparison of odometry and sensing data, the external parameters of the camera can be calibrated. Furthermore, an evaluation index (virtual sensing error) has been developed for the selection and optimization of test motions to obtain good calibration performance. All the test motion patterns are computed offline in advance and saved in a database, which greatly shorten the calibration time. Simulations and experiments verified the effectiveness of the proposed method.     

Roadmap Coverage Improvement Using a Node Rearrangement Method for Mobile Robot Path Planning

Abstract

This paper proposes a method to efficiently abstract the traversable regions of a bounded two-dimensional environment using the probabilistic roadmap (PRM) to plan the path for a mobile robot. The proposed method uses centroidal Voronoi tessellation to autonomously rearrange the positions of initially randomly generated nodes. The PRM using the rearranged nodes covers most of the traversable regions in the environment and regularly divides them. The rearranged roadmap reduces the search space of a graph search algorithm and helps to promptly answer arbitrary queries in the environment. The mobile robot path planner using the proposed rearranged roadmap was integrated with a local planner that considers the kinematic properties of a mobile robot, and the efficiency and the safety of the paths were verified by simulation.     

Modelling linear fractional transportation problem in belief degree—based uncertain environment

A linear fractional transportation problem in uncertain environment is studied in this paper where the uncertain parameters of the problem are of belief degree—based uncertainty. For the first time, this type of uncertainty is considered for the linear fractional transportation problem. Belief degree—based uncertainty is useful for the cases that no historical information of an uncertain event is available. Zigzag type uncertainty distribution is used to show the belief degree—based uncertainty of the parameters of the problem. As solution methodology, the uncertain linear fractional transportation problem is converted to a crisp form using three approaches of expected value model, expected value and chance-constrained model, and chance-constrained model, separately. An extensive computational study on a real illustrative example shows the efficiency of the proposed formulation and the conversion approaches. The sensitivity analysis over the example illustrates the high dependency of the objective function value to the changes of the confidence level values of the chance constraints in the expected value and chance-constrained programming approach and the chance-constrained programming approach.     

Virtual Chassis for Snake Robots: Definition and Applications

Abstract

Intuitively representing the motion of a snake robot is difficult. This is in part because the internal shape changes that the robot uses to locomote involve the entire body and no single point on the robot intuitively represents the robot’s pose at all times. To address this issue, we present a method of defining body coordinate frames that departs from the typical convention of rigidly fixing a frame to a link on the robot, and instead define a body frame that is based on the averaged position of all of the robot’s links. This averaged frame serves as a virtual chassis that effectively isolates the internal motion of the robot’s shape changes from the external motion, due to the robot’s interaction with its surroundings. This separation of motion allows much simpler models—such as those derived for wheeled vehicles—to accurately approximate the motion of the robot as it moves through the world. We demonstrate the practical advantages of using the virtual chassis body frame by estimating the pitch and roll of a snake robot undergoing dynamic motion by fusing readings from its internal encoders, gyros, and accelerometers with an extended Kalman filter.     

Near-future perception system: Previewed Reality

ABSTRACT

The present paper introduces a near-future perception system called Previewed Reality. In a co-existence environment of a human and a robot, unexpected collisions between the human and the robot must be avoided to the extent possible. In many cases, the robot is controlled carefully so as not to collide with a human. However, it is almost impossible to perfectly predict human behavior in advance. On the other hand, if a user can determine the motion of a robot in advance, he/she can avoid a hazardous situation and exist safely with the robot. In order to ensure that a user perceives future events naturally, we developed a near-future perception system named Previewed Reality. Previewed Reality consists of an informationally structured environment, a VR display or an AR display, and a dynamics simulator. A number of sensors are embedded in an informationally structured environment, and information such as the position of furniture, objects, humans, and robots, is sensed and stored structurally in a database. Therefore, we can forecast possible subsequent events using a robot motion planner and a dynamics simulator and can synthesize virtual images from the viewpoint of the user, which will actually occur in the near future. The viewpoint of the user, which is the position and orientation of a VR display or an AR display, is also tracked by an optical tracking system in the informationally structured environment, or the SLAM technique on an AR display. The synthesized images are presented to the user by overlaying these images on a real scene using the VR display or the AR display. This system provides human-friendly communication between a human and a robotic system, and a human and a robot can coexist safely by intuitively showing the human possible hazardous situations in advance.     

Recognition, prediction, and planning for assisted teleoperation of freeform tasks

The approach of inferring user’s intended task and optimizing low-level robot motions has promise for making robot teleoperation interfaces more intuitive and responsive. But most existing methods assume a finite set of candidate tasks, which limits a robot’s functionality. This paper proposes the notion of freeform tasks that encode an infinite number of possible goals (e.g., desired target positions) within a finite set of types (e.g., reach, orient, pick up). It also presents two technical contributions to help make freeform UIs possible. First, an intent predictor estimates the user’s desired task, and accepts freeform tasks that include both discrete types and continuous parameters. Second, a cooperative motion planner continuously updates the robot’s trajectories to achieve the inferred tasks by repeatedly solving optimal control problems. The planner is designed to respond interactively to changes in the indicated task, avoid collisions in cluttered environments, handle time-varying objective functions, and achieve high-quality motions using a hybrid of numerical and sampling-based techniques. The system is applied to the problem of controlling a 6D robot manipulator using 2D mouse input in the context of two tasks: static target reaching and dynamic trajectory tracking. Simulations suggest that it enables the robot to reach intended targets faster and to track intended trajectories more closely than comparable techniques.     

An improved model for swarm robotics in surveillance based on cellular automata and repulsive pheromone with discrete diffusion

ABSTRACT

An inverted ant cellular automata model called IACA-DI is proposed for the coordination of a swarm of robots performing the surveillance task. The swarm communicate indirectly through the repulsive pheromone, which is available as neighborhood information. The pheromone is deposited at each time step by each robot over its neighborhood. The new model started from a previous one named IACA. However, a discrete modeling of the pheromone diffusion is used in IACA-DI returning a sparser distribution of the robots over the environment. Next movement decisions are based on stochastic cellular automata rules that use the pheromone levels in the neighborhood to perform a probabilistic draw. While in IACA all the neighborhood cells participate in this draw, just a subgroup of them participate in the IACA-DI. It is formed by elite cells ? those with the lowest pheromone levels - and some random selected ones. Besides, the cell that keeps the current robot’s direction receives an increment in its probability to be chosen, giving an inertial tendency to the robot motion. Simple simulations were performed enabling to refine parameters and to choose the better strategies. After this refinement, the resultant model was implemented in the simulation platform Webots? aiming to evaluate IACA-DI with real-world robotic architecture in more realistic scenarios.

IACA-DI is a new model for the coordination of robot swarms performing the surveillance task. It is based on cellular automata modeling and the swarm communicate indirectly through the repulsive pheromone deposited by the robots in the environment cells. Letters (a) and (b) show two snapshots from a simulation of a 3-robots swarm performing the surveillance task. The robots start at random positions in an environment composed by 7 rooms in (a). Thus, based on the IACA-DI decisions, they start to make steps to explore the environment aiming to cover all the rooms in a short interval of time. The trace of each robot after 100 time steps is shown in (b) by representing each individual trajectory with a different color. The behavior of each robot is managed by the IACA-DI model, which can be represented by the FSM with 4 states in (c). Different strategies and formulations were investigated for the two major states ‘next position decision’ and ‘pheromone deposition’. The resultant IACA-DI model is analyzed here using simulations performed in Webots? platform -as the snapshots shown in (a) and (b) -with real-world robotic architectures.     

Robot task plan representation by Petri nets: modelling, identification, analysis and execution

In this paper we introduce a framework to represent robot task plans based on Petri nets. Our approach enables modelling a robot task, analysing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The overall model is obtained from the composition of simple models, leading to a modular approach. Analysis is applied to a closed loop between the robot controller and the environment Petri net models. We focus here on the quantitative properties, captured by stochastic Petri net models. Furthermore, we introduce a method to identify the environment and action layer parameters of the stochastic Petri net models from real data, improving the significance of the model. The framework building blocks and a single-robot task model are detailed. Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining, through well-known analysis methods for stochastic Petri nets, relevant properties of the task plan applied to a particular environment.     

Sensor-based obstruction avoidance technique for a mobile robot

This article describes a sensor-based obstruction avoidance technique. This technique, if implemented on the on-board computer of a mobile robot, would enable the robot to move through an unknown environment. The proposed approach is driven by sensory data. The robot thus senses and adapts to the changes in the environment. The software also does path planning. As more information about the environment is obtained the robot's path planning capabilities improve. Illustrative examples are used to describe the algorithms.     

Maximizing Reward in a Non-Stationary Mobile Robot Environment

The ability of a robot to improve its performance on a task can be critical, especially in poorly known and non-stationary environments where the best action or strategy is dependent upon the current state of the environment. In such systems, a good estimate of the current state of the environment is key to establishing high performance, however quantified. In this paper, we present an approach to state estimation in poorly known and non-stationary mobile robot environments, focusing on its application to a mine collection scenario, where performance is quantified using reward maximization. The approach is based on the use of augmented Markov models (AMMs), a sub-class of semi-Markov processes. We have developed an algorithm for incrementally constructing arbitrary-order AMMs on-line. It is used to capture the interaction dynamics between a robot and its environment in terms of behavior sequences executed during the performance of a task. For the purposes of reward maximization in a non-stationary environment, multiple AMMs monitor events at different timescales and provide statistics used to select the AMM likely to have a good estimate of the environmental state. AMMs with redundant or outdated information are discarded, while attempting to maintain sufficient data to reduce conformation to noise. This approach has been successfully implemented on a mobile robot performing a mine collection task. In the context of this task, we first present experimental results validating our reward maximization performance criterion. We then incorporate our algorithm for state estimation using multiple AMMs, allowing the robot to select appropriate actions based on the estimated state of the environment. The approach is tested first with a physical robot, in a non-stationary environment with an abrupt change, then with a simulation, in a gradually shifting environment.     

Visuo-inertial stabilization in space-variant binocular systems

Stabilization of gaze is a major functional prerequisite for robots exploring the environment. The main reason for a “steady-image” requirement is to prevent the robot’s own motion to compromise its “visual functions”. In this paper we present an artificial system, the LIRA robot head, capable of controlling its cameras/eyes to stabilize gaze. The system features a stabilization mechanism relying on principles exploited by natural systems: an inertial sensory apparatus and images of space-variant resolution. The inertial device measures angular velocities and linear acceleration along the vertical and horizontal fronto-parallel axes. The space-variant image geometry facilitates real-time computation of optic flow and the extraction of first-order motion parameters. Experiments which describe the performance of the LIRA robot head are presented. The results show that the stabilization mechanism improves the reactivity of the system to changes occurring suddenly at new spotted locations.