A dragline-forming mobile robot inspired by spiders.

Mobility of wheeled or legged machines can be significantly increased if they are able to move from a solid surface into a three-dimensional space. Although that may be achieved by addition of flying mechanisms, the payload fraction will be the limiting factor in such hybrid mobile machines for many applications. Inspired by spiders producing draglines to assist locomotion, the paper proposes an alternative mobile technology where a robot achieves locomotion from a solid surface into a free space. The technology resembles the dragline production pathway in spiders to a technically feasible degree and enables robots to move with thermoplastic spinning of draglines. As an implementation, a mobile robot has been prototyped with thermoplastic adhesives as source material of the draglines. Experimental results show that a dragline diameter range of 1.17-5.27 mm was achievable by the 185 g mobile robot in descending locomotion from the solid surface of a hanging structure with a power consumption of 4.8 W and an average speed of 5.13 cm min(-1). With an open-loop controller consisting of sequences of discrete events, the robot has demonstrated repeatable dragline formation with a relative deviation within -4% and a length close to the metre scale.

An Obstacle Cluster Based Data Fusion Method for Mobile Robot

首先给出了一种通过融合多个超声波传感器和一台激光全局定位系统的数据建立机器人环境地图的方法 ,并在此基础上 ,首次提出了机器人在非结构环境下识别障碍物的一种新方法 ,即基于障碍物群的方法 .该方法的最大特点在于它可以更加简洁、有效地提取和描述机器人的环境特征 ,这对于较好地实现机器人的导航、避障 ,提高系统的自主性和实时性是至关重要的 .大量的实验结果表明了该方法的有效性 .

Redundant Sensors for Mobile Robot Navigation

Redundant sensors are needed on a mobile robot so that the accuracy with which it perceives its surroundings can be increased. Sonar and infrared sensors are used here in tandem, each compensating for deficiencies in the other. The robot combines the data from both sensors to build a representation which is more accurate than if either sensor were used alone. Another representation, the curvature primal sketch, is extracted from this perceived workspace and is used as the input to two path planning programs: one based on configuration space and one based on a generalized cone formulation of free space.

Evolving mobile robot behaviours in simulation -- a comparison

Walker,J. and Wilson,M.S., 'Evolving mobile robot behaviours in simulation -- a comparison', Towards Intelligent Mobile Robots; Proceedings of the 3rd annual British conference on autonomous mobile robotics and autonomous systems (TIMR'01), 2001

Unsupervised Neural Network for the Control of a Mobile Robot

This article introduces an unsupervised neural architecture for the control of a mobile robot. The system allows incremental learning of the plant during robot operation, with robust performance despite unexpected changes of robot parameters such as wheel radius and inter-wheel distance. The model combines Vector associative Map (VAM) learning and associate learning, enabling the robot to reach targets at arbitrary distances without knowledge of the robot kinematics and without trajectory recording, but relating wheel velocities with robot movements.

Estimating the Heading of a Husky Mobile Robot with a LiDAR Compass based on Direction Maps

Without an absolute position sensor (e.g., GPS), an accurate heading estimate is necessary for proper localization of an autonomous unmanned vehicle or robot. This paper introduces direction maps (DMs), which represent the directions of only dominant surfaces of the vehicle’s environment and can be created with negligible effort. Given an environment with reoccurring surface directions (e.g., walls, buildings, parked cars), lines extracted from laser scans can be matched with a DM to provide an extremely lightweight heading estimate that is shown, through experimentation, to drastically reduce the growth of heading errors. The algorithm was tested using a Husky A200 mobile robot in a warehouse environment over traverses hundreds of metres in length. When a simple a priori DM was provided, the resulting heading estimation showed virtually no error growth.

A mobile robot platform for open learning based on serious games and remote laboratories

Within the pedagogical community, Serious Games have arisen as a viable alternative to traditional course-based learning materials. Until now, they have been based strictly on software solutions. Meanwhile, research into Remote Laboratories has shown that they are a viable, low-cost solution for experimentation in an engineering context, providing uninterrupted access, low-maintenance requirements, and a heightened sense of reality when compared to simulations. This paper will propose a solution where both approaches are combined to deliver a Remote Laboratory-based Serious Game for use in engineering and school education. The platform for this system is the WebLab-Deusto Framework, already well-tested within the remote laboratory context, and based on open standards. The laboratory allows users to control a mobile robot in a labyrinth environment and take part in an interactive game where they must locate and correctly answer several questions, the subject of which can be adapted to educators' needs. It also integrates the Google Blockly graphical programming language, allowing students to learn basic programming and logic principles without needing to understand complex syntax.

An Encoded Infrared Sheet Of Light Navigational Beacon System For Precise Localization Of Indoor Mobile Robot Vehicles

A new localization approach to increase the navigational capabilities and object manipulation of autonomous mobile robots, based on an encoded infrared sheet of light beacon system, which provides position errors smaller than 0.02m is presented in this paper. To achieve this minimal position error, a resolution enhancement technique has been developed by utilising an inbuilt odometric/optical flow sensor information. This system respects strong low cost constraints by using an innovative assembly for the digitally encoded infrared transmitter. For better guidance of mobile robot vehicles, an online traffic signalling capability is also incorporated. Other added features are its less computational complexity and online localization capability all these without any estimation uncertainty. The constructional details, experimental results and computational methodologies of the system are also described

A Distributed Model for Mobile Robot Environment-Learning and Navigation

A distributed method for mobile robot navigation, spatial learning, and path planning is presented. It is implemented on a sonar-based physical robot, Toto, consisting of three competence layers: 1) Low-level navigation: a collection of reflex-like rules resulting in emergent boundary-tracing. 2) Landmark detection: dynamically extracts landmarks from the robot's motion. 3) Map learning: constructs a distributed map of landmarks. The parallel implementation allows for localization in constant time. Spreading of activation computes both topological and physical shortest paths in linear time. The main issues addressed are: distributed, procedural, and qualitative representation and computation, emergent behaviors, dynamic landmarks, minimized communication.

Differential epipolar constraint in mobile robot egomotion estimation

The estimation of camera egomotion is a well established problem in computer vision. Many approaches have been proposed based on both the discrete and the differential epipolar constraint. The discrete case is mainly used in self-calibrated stereoscopic systems, whereas the differential case deals with a unique moving camera. The article surveys several methods for mobile robot egomotion estimation covering more than 0.5 million samples using synthetic data. Results from real data are also given

Experimental Modeling and Control Strategies on an Open Mobile Robot Platform PRIM

This research work deals with the problem of modeling and design of low level speed controller for the mobile robot PRIM. The main objective is to develop an effective educational tool. On one hand, the interests in using the open mobile platform PRIM consist in integrating several highly related subjects to the automatic control theory in an educational context, by embracing the subjects of communications, signal processing, sensor fusion and hardware design, amongst others. On the other hand, the idea is to implement useful navigation strategies such that the robot can be served as a mobile multimedia information point. It is in this context, when navigation strategies are oriented to goal achievement, that a local model predictive control is attained. Hence, such studies are presented as a very interesting control strategy in order to develop the future capabilities of the system

Knowledge integration within an applied mobile robot summer course

This paper presents the use of a mobile robot platform as an innovative educational tool in order to promote and integrate different curriculum knowledge. Hence, it is presented the acquired experience within a summer course named ldquoapplied mobile roboticsrdquo. The main aim of the course is to integrate different subjects as electronics, programming, architecture, perception systems, communications, control and trajectory planning by using the educational open mobile robot platform PRIM. The summer course is addressed to a wide range of student profiles. However, it is of special interests to the students of electrical and computer engineering around their final academic year. The summer course consists of the theoretical and laboratory sessions, related to the following topics: design & programming of electronic devices, modelling and control systems, trajectory planning and control, and computer vision systems. Therefore, the clues for achieving a renewed path of progress in robotics are the integration of several knowledgeable fields, such as computing, communications, and control sciences, in order to perform a higher level reasoning and use decision tools with strong theoretical base

Predictive motion control of a mirosot mobile robot

This paper discusses predictive motion control of a MiRoSoT robot. The dynamic model of the robot is deduced by taking into account the whole process - robot, vision, control and transmission systems. Based on the obtained dynamic model, an integrated predictive control algorithm is proposed to position precisely with either stationary or moving obstacle avoidance. This objective is achieved automatically by introducing distant constraints into the open-loop optimization of control inputs. Simulation results demonstrate the feasibility of such control strategy for the deduced dynamic model