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The purpose of this paper is to describe a novel application of the recently introduced concept from computer vision to self‐localization of a walking robot in unstructured environments. The technique described in this paper enables a walking robot with a monocular vision system (single camera) to obtain precise estimates of its pose with regard to the six degrees of freedom. This capability is essential in search and rescue missions in collapsed buildings, polluted industrial plants, etc.

The Parallel Tracking and Mapping (PTAM) algorithm and the Inertial Measurement Unit (IMU) are used to determine the 6‐d.o.f. pose of a walking robot. Bundle‐adjustment‐based tracking and structure reconstruction are applied to obtain precise camera poses from the monocular vision data. The inclination of the robot's platform is determined by using IMU. The self‐localization system is used together with the RRT‐based motion planner, which allows to walk autonomously on rough, previously unknown terrain. The presented system operates on‐line on the real hexapod robot. Efficiency and precision of the proposed solution are demonstrated by experimental data.

The PTAM‐based self‐localization system enables the robot to walk autonomously on rough terrain. The software operates on‐line and can be implemented on the robot's on‐board PC. Results of the experiments show that the position error is small enough to allow robust elevation mapping using the laser scanner. In spite of the unavoidable feet slippages, the walking robot which uses PTAM for self‐localization can precisely estimate its position and successfully recover from motion execution errors.

So far the presented self‐localization system was tested in limited‐scale indoor experiments. Experiments with more realistic outdoor scenarios are scheduled as further work.

Precise self‐localization may be one of the most important factors enabling the use of walking robots in practical USAR missions. The results of research on precise self‐localization in 6‐d.o.f. may be also useful for autonomous robots in other application areas: construction, agriculture, military.

The vision‐based self‐localization algorithm used in the presented research is not new, but the contribution lies in its implementation/integration on a walking robot, and experimental evaluation in the demanding problem of precise self‐localization in rough terrain.

In this research, the authors established a hierarchical motion planner for quadruped locomotion, which enables a parallel wheel-quadruped robot, the “BIT-NAZA” robot, to traverse rough three-dimensional (3-D) terrain.

Presented is a novel wheel-quadruped mobile robot with parallel driving mechanisms and based on the Stewart six degrees of freedom (6-DOF) platform. The task for traversing rough terrain is decomposed into two prospects: one is the configuration selection in terms of a local foothold cost map, in which the kinematic feasibility of parallel mechanism and terrain features are satisfied in heuristic search planning, and the other one is a whole-body controller to complete smooth and continuous motion transitions.

A fan-shaped foot search region focuses on footholds with a strong possibility of becoming foot placement, simplifying computation complexity. A receding horizon avoids kinematic deadlock during the search process and improves robot adaptation.

Both simulation and experimental results validated the proposed scenario available and appropriate for quadruped locomotion to traverse challenging 3-D terrains.

This paper analyzes kinematic workspace for a parallel robot with 6-DOF Stewart mechanism on both body and foot. A fan-shaped foot search region enhances computation efficiency. Receding horizon broadens the preview search to decrease the possibility of deadlock minima resulting from terrain variation.

To present a stereo matching algorithm which satisfies the need of visual navigation on outdoor natural terrain for lunar rover or other mobile robots.

A feature‐assisted matching algorithm is presented to generate dense and accurate disparity map of natural terrain. Multi‐feature matching strategy produces reliable matching results for edge points. Disparity monotony constraint is derived and other geometrical constraints are introduced. With these constraints the edge‐matching results are used to limit the search region in area‐matching. As a result the algorithm produces dense disparity maps with fairly high accuracy and demonstrates advantages over straightforward area‐matching algorithm in improving matching accuracy.

With the help of several constraints, the feature‐assisted matching algorithm performs well in the matching of stereo image pairs of natural terrain.

The algorithm focus on improving the accuracy of stereo image pairs matching of natural terrain and computation complexity is not an important designing factor. Only with the assistance of special hardware or other technique can the algorithm be used for real‐time navigation.

The algorithm is able to produce dense disparity map of natural terrain with rather high accuracy and can be used for the navigation of lunar rover or other outdoor mobile robots.

The paper provides a new approach to produce accurate and dense disparity map of natural terrain.

Stereo vision is an attractive perception technique for mobile robots navigation. Stereo matching is a crucial part of stereo vision and its precision dominates the precision of reconstruction. Based on a geometry constraint applicable to natural terrain, the purpose of this paper is to present a multi‐stage stereo matching algorithm to improve matching accuracy.

In the multi‐stage matching algorithm, points with larger intensity gradient are matched in earlier stages. Using several constraints and statistical means, information from earlier stages is utilized to assist in matching of later stages to improve matching accuracy.

The multi‐stage matching algorithm improves the matching accuracy of stereo pairs of natural terrain in various conditions.

The algorithm demonstrates advantages over area‐matching algorithm both in matching accuracy and computation efficiency. However, if used for real‐time navigation, it still needs the assistance of specialized hardware or window selection technique.

The algorithm is able to produce dense disparity maps of natural terrain with fairly high accuracy and can be used for the navigation of planetary rover or other outdoor mobile robots.

The paper provides a new approach to produce accurate and dense disparity maps of natural terrain, which laid the foundation for its use in outdoor mobile robots navigation.

The purpose of this paper is to present a novel approach in terrain following (TF) flight using fuzzy logic. The fuzzy controller as presented in this work decides where and how the aircraft needs to change its altitude. The fast decision‐making nature of this method promises real‐time applications even for tough terrains in terms of shape and peculiarities. The method could always assist to design trajectories in an off‐line manner.

To achieve the aforementioned goal, the method effectively incorporates the dynamics of the aircraft. Basically, the mathematical method employs special relationships among existing slope of the terrain and its derivative together with aircraft flying speed and height above the ground to construct suitable fuzzy rules. The fuzzification method is based on Sugeno and three rule‐sets are used for fuzzy structure. These rules are implemented using Fuzzy Logic Toolbox in MATLAB.

Different case studies conducted for flights in XZ‐plane show the effectiveness of the method as compared to other existing methods available to the authors. The results illustrate a good tracking based on the fuzzy approach while using both 18 and 27 rules with respect to the optimal approach. Furthermore, it is shown that decreasing number of rules from 27 to 18 rules causes only minor changes in the solution.

The current work offers a new approach in low‐level flights where maintaining a suitable height above the ground is essential. This is especially important for civil aircraft approaching an airport with low or non‐visibility and during aborted landing manoeuvres. The domain of the current work is however confined to only planning of TF manoeuvres. Nevertheless, the work could be expanded into TF/terrain avoidance and three‐dimensional manoeuvres which are not in the scope of the current work.

The current work addresses the problems associated with low‐level flight; such as TF using artificial intelligence and fuzzy logic. The provided intelligence helps the aircraft conduct TF manoeuvres by understanding the general patterns of the existing terrain. The method is fast enough to be applied for real‐time applications.

As wheeled mobile robots find increasing use in outdoor applications, it becomes more important to reduce energy consumption to perform more missions efficiently with limit energy supply. The purpose of this paper is to survey the current state-of-the-art on energy-efficient motion planning (EEMP) for wheeled mobile robots.

The use of wheeled mobile robots has been increased to replace humans in performing risky missions in outdoor applications, and the requirement of motion planning with efficient energy consumption is necessary. This study analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present. The dynamic constraints play a key role in EEMP problem, which derive the power model related to energy consumption. The surveyed approaches differ in the used steering mechanisms for wheeled mobile robots, in assumptions on the structure of the environment and in computational requirements. The comparison among different EEMP methods is proposed in optimal, computation time and completeness.

According to lots of literature in EEMP problem, the research results can be roughly divided into online real-time optimization and offline optimization. The energy consumption is considered during online real-time optimization, which is computationally expensive and time-consuming. The energy consumption model is used to evaluate the candidate motions offline and to obtain the optimal energy consumption motion. Sometimes, this optimization method may cause local minimal problem and even fail to track. Therefore, integrating the energy consumption model into the online motion planning will be the research trend of EEMP problem, and more comprehensive approach to EEMP problem is presented.

EEMP is closely related to robot’s dynamic constraints. This paper mainly surveyed in EEMP problem for differential steered, Ackermann-steered, skid-steered and omni-directional steered robots. Other steering mechanisms of wheeled mobile robots are not discussed in this study.

The survey of performance of various EEMP serves as a reference for robots with different steering mechanisms using in special scenarios.

This paper analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present.

The purpose of this paper is to improve the understanding of university‐industry research collaboration through the development of a new process model.

A literature review was carried out on collaborative partnering and supporting factors namely social capital and the role of knowledge. Empirical research involved a series of 32 structured interviews with relevant stakeholders, with subsequent grouping and conceptualisation allowing common themes to be identified and a new process model to be proposed.

The study finds that there is a lack of integrative frameworks for the management of research collaborations. Through building on the suggested best practice described in the paper, application of the model to the management of an engineering research programme has allowed the benefits of this approach as well as some of the underlying issues to be explored in detail.

The research focused on university‐industry research collaborations and although it may be applicable to other forms of collaborations, e.g. industry‐to‐industry, there could be features that are particular to the area under investigation.

A model has been proposed, which is a logical methodology that can be utilised by practitioners from both academia and industry in order to improve the process of research collaboration and facilitate more effective transfer of knowledge.

The model builds on previous literature on alliance and collaboration management but crucially is based on an innovative new process‐based methodology, which provides practitioners with a “route map” of how to develop and manage research collaborations. The model uses a holistic approach to collaboration through capturing process, knowledge and social elements.

The purpose of this paper is to detect traversable regions surrounding a mobile robot by computing terrain unevenness using the range data obtained from a single 3D scan.

The geometry of acquiring range data from a 3D scan is exploited to probe the terrain and extract traversable regions. Nature of terrain under each scan point is quantified in terms of an unevenness value, which is computed from the difference in range of scan point with respect to its neighbours. Both radial and transverse unevenness values are computed and compared with threshold values at every point to determine if the point belongs to a traversable region or an obstacle. A region growing algorithm spreads like a wavefront to join all traversable points into a traversable region.

This simple method clearly distinguishes ground and obstacle points. The method works well even in presence of terrain slopes or when the robot experiences pitch and roll.

The method applies on single 3D scans and not on aggregated point cloud in general.

The method has been tested on a mobile robot in outdoor environment in our research centre.

This method, along with advanced navigation schemes, can reduce human intervention in many mobile robot applications including unmanned ground vehicles.

Range difference between scan points has been used earlier for obstacle detection, but no methodology has been developed around this concept. The authors propose a concrete method based on computation of radial and transverse unevenness at every point and detecting obstacle edges using range-dependent threshold values.

To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.

The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.

The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.

This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

Two and one half‐dimensional (2.5D) grid maps are useful for navigation in outdoor environment or on non‐flat surface. However, little attention has been given to how to find an optimal path in a 2.5D grid map. The purpose of this paper is to develop a path‐planning method in a 2.5D grid map, which aims to provide an efficient solution to robot path planning no matter whether the robot is equipped with the prior knowledge of the environment.

A 2.5D grid representation is proposed to model non‐flat surface for mobile robots. According to the graph extracted from the 2.5D grid map, an improved searching approach derived from A* algorithm is presented for the shortest path planning. With reasonable assumption, the approach is improved for the path planning in unknown environment.

It is confirmed by experiments that the proposed planning approach provide a solution to the problem of optimal path planning in 2.5 grid maps. Furthermore, the experiment results demonstrate that our 2.5D D* method leads to more efficient dynamic path planning for navigation in unknown environment.

This paper proposes a path‐planning approach in a 2.5D grid map which is used to represent a non‐flat surface. The approach is capable of efficient navigation no matter whether the global environmental information is available at the beginning of exploration.