Ground Mobile Robots Research Articles

Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments

Cloud robotics is the application of cloud computing concepts to robotic systems. It utilizes modern cloud computing infrastructure to distribute computing resources and datasets. Cloud‐based real‐time outsourcing localization architecture is proposed in this paper to allow a ground mobile robot to identify its location relative to a road network map and reference images in the cloud. An update of the road network map is executed in the cloud, as is the extraction of the robot‐terrain inclination (RTI) model as well as reference image matching. A particle filter with a network‐delay‐compensation localization algorithm is executed on the mobile robot based on the local RTI model and the recognized location both of which are sent from the cloud. The proposed methods are tested in different challenging outdoor scenarios with a ground mobile robot equipped with minimal onboard hardware, where the longest trajectory was 13.1 km. Experimental results show that this method could be applicable to large‐scale outdoor environments for autonomous robots in real time.

Monte Carlo planning for active object classification

Classifying objects in complex unknown environments is a challenging problem in robotics and is fundamental in many applications. Modern sensors and sophisticated perception algorithms extract rich 3D textured information, but are limited to the data that are collected from a given location or path. We are interested in closing the loop around perception and planning, in particular to plan paths for better perceptual data, and focus on the problem of planning scanning sequences to improve object classification from range data. We formulate a novel time-constrained active classification problem and propose solution algorithms that employ a variation of Monte Carlo tree search to plan non-myopically. Our algorithms use a particle filter combined with Gaussian process regression to estimate joint distributions of object class and pose. This estimator is used in planning to generate a probabilistic belief about the state of objects in a scene, and also to generate beliefs for predicted sensor observations from future viewpoints. These predictions consider occlusions arising from predicted object positions and shapes. We evaluate our algorithms in simulation, in comparison to passive and greedy strategies. We also describe similar experiments where the algorithms are implemented online, using a mobile ground robot in a farm environment. Results indicate that our non-myopic approach outperforms both passive and myopic strategies, and clearly show the benefit of active perception for outdoor object classification.

Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots

For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback–Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.

Caster-leg aided maneuver for negotiating surface discontinuities with a wheeled skid-steer mobile robot

Crossing surface discontinuities, such as ditches or curbs in urban environments, is a relevant navigation issue for ground mobile robots. This paper proposes a maneuver for negotiating surface discontinuities for a hybrid locomotion system consisting on a skid-steer wheeled vehicle with a single caster-leg mechanism. This maneuver is appropriate for gaps, steps, and ramps, both upwards and downwards. Furthermore, an analysis of tipover stability and steerability through the different states of the maneuver is performed, resulting in sensor-based stability margins that can be computed online. Experimental results have been obtained by means of realistic Simulink/ADAMS simulations and actual tests with the Lazaro mobile robot. The results confirm the effectiveness of the proposed maneuver as well as the applicability of stability margins.

Geometric Relation-Based Cognitive Sharing for Flying and Ground Mobile Robot Cooperation

Cognitive sharing of objects is fundamental in a heterogeneous robot system composed of a Unmanned Aerial Vehicle and a ground robot. Since the viewpoint of a UAV is greatly different from a ground robot, they may have different perceptions about the same objects. That makes it difficult to realize cognitive sharing. In this paper, we proposed a cognitive sharing method which is based on Geometric Relation-based Triangle Representations. The method is able to make a UAV and a ground robot identify the same object from similar objects without sharing appearance information in unstructured environment. To copy with the problem of increasing computational cost for the recognition of objects in the Region of Interest, entropy evaluation is employed to evaluate and select unique representations. We illustrated the proposed method with robots in real world.

Ground and Aerial Mutual Localization Using Anonymous Relative-Bearing Measurements

We present a decentralized algorithm for estimating mutual poses (relative positions and orientations) in a group of mobile robots. The algorithm uses relative-bearing measurements, which, for example, can be obtained from onboard cameras, and information about the motion of the robots, such as inertial measurements. It is assumed that all relative-bearing measurements are anonymous; i.e., each specifies a direction along which another robot is located but not its identity. This situation, which is often ignored in the literature, frequently arises in practice and remarkably increases the complexity of the problem. The proposed solution is based on a two-step approach: in the first step, the most likely unscaled relative configurations with identities are computed from anonymous measurements by using geometric arguments, while in the second step, the scale is determined by numeric Bayesian filtering based on the motion model. The solution is first developed for ground robots in SE (2) and then for aerial robots in SE (3). Experiments using Khepera III ground mobile robots and quadrotor aerial robots confirm that the proposed method is effective and robust w.r.t. false positives and negatives of the relative-bearing measuring process.

Trafficability Assessment of Deformable Terrain through Hybrid Wheel‐Leg Sinkage Detection

Off‐road ground mobile robots are widely used in diverse applications, both in terrestrial and planetary environments. They provide an efficient alternative, with lower risk and cost, to explore or to transport materials through hazardous or challenging terrain. However, nongeometric hazards that cannot be detected remotely pose a serious threat to the mobility of such robots. A prominent example of the negative effects these hazards can have is found on planetary rover exploration missions. They can cause a serious degradation of mission performance at best and complete immobilization and mission failure at worst. To tackle this issue, the work presented in this paper investigates the novel application of an existing enhanced‐mobility locomotion concept, a hybrid wheel‐leg equipped by a lightweight micro‐rover, for in situ characterization of deformable terrain and online detection of nongeometric hazards. This is achieved by combining an improved vision‐based approach and a new ranging‐based approach to wheel‐leg sinkage detection. In addition, the paper proposes an empirical model, and a parametric generalization, to predict terrain trafficability based on wheel‐leg sinkage and a well‐established semiempirical terramechanics model. The robustness and accuracy of the sinkage detection methods implemented are tested in a variety of conditions, both in the laboratory and in the field, using a single wheel‐leg test bed. The sinkage‐trafficability model is developed based on experimental data using this test bed and then validated onboard a fully mobile robot through experimentation on a range of dry frictional soils that covers a wide spectrum of macroscopic physical characteristics.

Robust Range-Only SLAM for Unmanned Aerial Systems

Range-only sensors, in contrast to bearing-only sensors or LIDAR sensors are sensors which only provides distance between two points but not bearing information. The reduced amount of information provided by this sensors makes their application in Simultaneous Localization and Mapping (SLAM) problems specially challenging due to the lack of information. The complexity of the problem becomes higher when using noisy range-only sensors like radio-based where the multi-path effect may introduce range outliers, hence reducing the precision of the SLAM solution. Furthermore, the application of Range-only SLAM to aerial systems implies a higher level of complexity due to the introduction to a 3th dimensionality in contrast to ground mobile robots where the altitude dimension is not necessary in most applications. This paper presents an efficient and robust method to the Range-only SLAM (RO-SLAM) problem when applied to aerial vehicles and radio-based range-only measurements. The method is based on a pre-filtering algorithm which is able to detect and remove range outliers and on the application of a reduced parameterization which scales better with 3D RO-SLAM problems. The method is validated with real experiments involving an aerial vehicle and multiple radio-based range-only sensors.

Road detection in image by fusion laser points based on fuzzy SVM for a small ground mobile robot

Road detection is still full of challenge for a small ground mobile robot with limited load capacity and computing resources which works in complex outdoor environment. This paper proposes a road detection method based on fuzzy support vector machine with on-line updating and retraining strategy. The algorithm extracts multi feature in image and trains a fuzzy support vector machine road classifier off-line by using few training samples. Then it detects road in laser points using a fuzzy clustering method based on maximum entropy principle. After calibrating the camera and laser range finder, and project laser points into the image, the algorithm chooses road samples with high confidence automatically according to range data and designs a rule to retraining the FSVM on-line when needed to improve its environmental adaptability. Experiments in outdoor campus environment indicate that the proposed algorithm is effective.

Conditions for target tracking with range-only information

This paper addresses the problem of guiding a mobile robot towards a target using only range sensors. The bearing information is not available. The target can be stationary or moving. It can be the source of some gas leakage or nuclear radiation or it can be some landmark or beacon or any manoeuvring vehicle. The mobile robot can be a ground vehicle or an aerial vehicle flying at a fixed altitude. In literature, many different strategies are proposed which use the range only measurement but they involve estimation of different parameters or have switching control strategy which make them difficult to implement. We propose two sets of conditions, one for stationary target and another for both stationary and moving target. Any control strategy, that will satisfy these conditions, can bring the robot arbitrarily close to the target. There are no restrictions on the initial conditions. Estimation of any parameter is not required. Some candidate controllers are presented that included continuous controllers and switching controllers. Simulations are carried out with these controllers to validate our result with and without measurement noise. Experimental results with ground mobile robot are presented.

Functional Redesign of Mantis 2.0, a Hybrid Leg-Wheel Robot for Surveillance and Inspection

The paper discusses the redesign of the second version of the Mantis hybrid leg-wheel mobile robot, conceived for surveillance and inspection tasks in unstructured indoor and outdoor environments. This small-scale ground mobile robot is characterized by a main body equipped with two front actuated wheels, a passive rear axle and two rotating legs. Motion on flat and even ground is purely wheeled in order to obtain high speed, high energetic efficiency and stable camera vision; only in case of obstacles or ground irregularities the front legs realize a mixed leg-wheel locomotion to increase the robot climbing ability; in particular, the outer profile of the legs, inspired by the praying mantis, is specially designed to climb square steps. The multibody simulations and the experimental tests on the first prototype have shown the effectiveness of the mixed leg-wheel locomotion not only for step climbing, but also on uneven and yielding terrains. Nevertheless, extensive experimental tests have shown that the front wheels may slip in the last phase of step climbing in case of contact with some materials. In order to overcome this problem, the leg design has been modified with the introduction of auxiliary passive wheels, which reduce friction between legs and step upper surface; these wheels are connected to the legs by one-way bearings, in order to rotate only when they are pulled by the front wheels, and remaining locked when they have to push forward the robot. The influence of the auxiliary wheels on the front wheels slippage is investigated by means of theoretical analysis and multibody simulations.

Real-time face tracking and recognition using the mobile robots

The terrain is complex and dynamic in a disaster. This paper aimed at constructing a multipose-specific feature model for online face recognition and tracking in a search and rescue operation. This paper proposes an integrated multipose face tracking and recognition system mounted on an unmanned aerial vehicle (UAV). The face model is constructed online by multi-scale block local binary pattern (MB-LBP) for face recognition. The generic and specific face models are further integrated for face tracking. Mechanisms for the online update of face models to retrieve loss of face tracking are also implemented. The results show that the proposed system achieves stable and robust tracking despite uncertainties (e.g. non-rigid human face, face expression changes, different face poses, complex background, varying illumination, partial visual occlusion, and pose changes). The target loss during tracking can be retrieved correctly. In face recognition, the multipose-specific face model can achieve an accuracy (above 70%). The results demonstrate the feasibility of the proof-of-concept using UAV or ground mobile robot (GMR) for real-time face recognition and tracking.

Swarm-based path-following for cooperative unmanned surface vehicles

This article proposes a swarm-based path-following guidance system for an autonomous marine multi-vehicle system. In particular, a team of unmanned surface vehicles is required to converge to and navigate along a desired reference path, while at the same time aggregating and maintaining a range-based formation configuration. First, a separate description is given for a swarm methodology, initially developed for small ground mobile robots and exploited to aggregate the robot team, and a virtual target–based path-following guidance system developed for unmanned surface vehicles, exploited to drive not the single vehicles but the robot formation as a whole. Then, the integration of the two proposed methodologies is reported and proven, in order to guarantee the feasibility and stability of the overall guidance framework. Simulative results are proposed to validate the effectiveness of the proposed methodology and to evaluate the system performances.

Mantis: hybrid leg-wheel ground mobile robot

Purpose – The aim of the research is the development of a small-scale ground mobile robot for surveillance and inspection; the main design goals are mobility in indoor environments with step climbing ability, pivoting around a vertical axis and without oscillations for stable vision, mobility in unstructured environments, low mechanical and control complexity. Design/methodology/approach – The proposed hybrid leg-wheel robot is characterized by a main body equipped with two actuated wheels and two praying Mantis rotating legs; a rear frame with two idle wheels is connected to the main body by a vertical revolute joint for steering; a second revolute joint allows the rear axle to roll. The geometrical synthesis of the robot has been performed using a nondimensional approach for generality's sake. Findings – The experimental campaign on the first prototype confirms the fulfilment of the design objectives; the robot can efficiently walk in unstructured environments realizing a mixed wheeled-legged locomotion. Practical implications – Thanks to the operative flexibility of Mantis in indoor and outdoor environments, the range of potential applications is wide: surveillance, inspection, monitoring of dangerous locations, intervention in case of terroristic attacks, military tasks. Originality/value – Different from other robots of similar size, Mantis combines high speed and energetic efficiency, stable vision, capability of climbing over high steps, obstacles and unevenness.

A Taxonomy of Vision Systems for Ground Mobile Robots

This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five years, a significant number of relevant papers have contributed to this subject. Firstly, a thorough review of the papers is proposed to discuss and classify both past and the most current approaches in the field. As a result, a global picture of the state of the art of the last five years is obtained. Moreover, the study of the articles is used to put forward a comprehensive taxonomy based on the most up-to-date research in ground mobile robotics. In this sense, the paper aims at being especially helpful to both budding and experienced researchers in the areas of vision systems and mobile ground robots. The taxonomy described is devised from a novel perspective, namely in order to respond to the main questions posed when designing robotic vision systems: why?, what for?, what with?, how?, and where? The answers are derived from the most relevant techniques described in the recent literature, leading in a natural way to a series of classifications that are discussed and contextualized. The article offers a global picture of the state of the art in the area and discovers some promising research lines.

The VIV: A Mobile Ground Robot with Variable Inertial Properties

The VIV: A Mobile Ground Robot with Variable Inertial Properties

The Application of Model Predictive Control (MPC) to Fast Systems such as Autonomous Ground Vehicles (AGV)

This paper investigates the application of Model Predictive Control (MPC) to fast systems such as Autonomous Ground Vehicles (AGV) or mobile robots. The control of Autonomous ground vehicles (AGV) is challenging because of nonholonomic constraints, uncertainties, speed, accuracy of controls and the vehicle's terrain of operation. Two nonlinear models: a car-like model and a bicycle model are considered. A Nonlinear MPC (NMPC) was developed. A trajectory tracking performance index for both models was studied. After thorough and extensive simulation, it is observed that both models are applicable in the context of NMPC and the constraints on model variables were adequately respected. The trajectories were successfully tracked and thus clearly indicate the efficiency and effectiveness of the MPC technique. In order to improve on speed and reduce the computational effort required for the optimization problem, a Linear MPC (LMPC) was implemented with both models. This is possible by successive linearization along the reference trajectory and formulating a quadratic optimization problem which is solved by implementing an interior-point quadratic programming algorithm. For both AGV models, analysis concerning the reduced computational efforts is presented in order to

1P1-H03 無人電動マルチロータ機搭載型土砂採取機構の開発(サーチ&レスキューロボット・メカトロニクス(1))

When an active volcano erupts, typically, a restricted area is set around the crater for safety. However, it is important to observe inside of the restricted area for a forecast of disasters, such as debris flow. There arc some proposals of visual observation methods in restricted areas, e.g. a tele-operated mobile ground robot with cameras. However, there is no sample-return method for obtaining volcaniclastic materials from such restricted areas. Therefore, in this research, we developed two sample-return devices that hang down from multi-rotor UAV. In this paper, we introduce our developed devices, and report indoor and outdoor experiments.

IMPLEMENTATION OF AUTONOMOUS NAVIGATION ALGORITHMS ON TWO-WHEELED GROUND MOBILE ROBOT

This study presents an effective navigation archite cture that combines ‘go-to-goal’, ‘avoid-obstacle’ and ‘follow-wall’ controllers into a full navigation sy stem. A MATLAB robot simulator is used to implement this navigation control algorithm. The robot in the simulator moves to a goal in the presence of conve x and non-convex obstacles. Experiments are carried out u sing a ground mobile robot, Dr Robot X80SV, in a typical office environment to verify successful imp lementation of the navigation architecture algorith m programmed in MATLAB. The research paper also demonstrates algorithms to achieve tasks such as ‘move to a point’, ‘move to a pose’, ‘follow a line’, ‘mo ve in a circle’ and ‘avoid obstacles’. These contro l algorithms are simulated using Simulink models.

A Modular Approach for a Family of Ground Mobile Robots

This paper deals with Epi.q, a family of mobile robots whose main characteristic is a wheel-legged hybrid locomotion. These multi-purpose robots can be successfully exploited for security and surveillance tasks. The document presents state of the art security robotics, the Epi.q mechanical architecture, the concept behind the robot driving unit, three prototypes and the design of a new one.