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This paper addresses the issue of trajectory planning for a free-floating dual-arm space robot, considering the constraints of collision avoidance while simultaneously minimizing the base position and attitude disturbance during on-orbit tasks. One arm is designated as the mission arm, tracking a prescribed path to complete specific goals, while the other serves as the balance arm, compensating for any base attitude disturbance. A collision-free path planning approach is presented for the mission arm, which utilizes a hybrid map, improved artificial potential field (IAPF) and adaptive coupling guidance method. The hybrid map in the mission arm joint space contains the coupling relationship between the deviation of the base position and the motion of the dual arms, along with obstacles transformed from Cartesian space. Then, to address the problem of the goals nonreachable with obstacles nearby (GNRON) of the artificial potential field (APF), a novel repulsive potential field is incorporated into the IAPF. To minimize the base position disturbance, a coupling guidance method is presented that enables the mission arm to move in areas with lower coupling factors on the hybrid map. Finally, simulations are conducted to demonstrate the effectiveness and superiority of the proposed hybrid map and coordinated trajectory planning approach. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs. [ABSTRACT FROM AUTHOR]

In last few years, chaotic maps are being used extensively by the researchers to build image cryptosystems. This paper proposes a novel Hybrid chaotic map, and discusses the implementation of an image encryption method that uses an existing Sine–Cosine (SC) chaotic map, and the proposed novel Hybrid chaotic map. The proposed image encryption method uses these two maps in pairs, but dynamically, in both permutation and diffusion phases, where if one map is used for permutation of one-pixel block based on dynamic selection, then the other map will be used for diffusion of the same pixel block. This encryption mechanism uses 128-bit shared key along with the 128-bit Initial vector. The initialization phase of this encryption scheme consists of the initialization of the intermediate key, seeds and the control parameters for the chaotic maps. After that, the two different chaotic initial sequences are created using the SC and Hybrid chaotic map, respectively, where sequence from one map will be used for permutation, and sequence from other map will be used for diffusion. The chaotic range of proposed Hybrid map lies between 5.5 to 15, and its chaotic properties have been validated using Lyapunov exponent, Bifurcation diagram, and Shannon Entropy. The results show that the Bifurcation diagram of the Hybrid map is uniform in entire range. Also, the Lyapunov Exponent of the Hybrid map is greater than 0, and the value of the Shannon entropy of the Hybrid map is close to 10. Also, the proposed image encryption scheme is capable of resisting different types of security attacks. [ABSTRACT FROM AUTHOR]

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs.

To address the practical navigation issues of rubber-tapping robots, this paper proposes an active navigation system guided by trunk detection for a rubber-tapping robot. A tightly coupled sliding-window-based factor graph method is proposed for pose tracking, which introduces normal distribution transform (NDT) measurement factors, inertial measurement unit (IMU) pre-integration factors, and prior factors generated by sliding window marginalization. To actively pursue goals in navigation, a distance-adaptive Euclidean clustering method is utilized in conjunction with cylinder fitting and composite criteria screening to identify tree trunks. Additionally, a hybrid map navigation approach involving 3D point cloud map localization and 2D grid map planning is proposed to apply these methods to the robot. Experiments show that our pose-tracking approach obtains generally better performance in accuracy and robustness compared to existing methods. The precision of our trunk detection method is 93% and the recall is 87%. A practical validation is completed in robot rubber-tapping tasks of a real rubber plantation. The proposed method can guide the rubber-tapping robot in complex forest environments and improve efficiency. [ABSTRACT FROM AUTHOR]

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To address the practical navigation issues of rubber-tapping robots, this paper proposes an active navigation system guided by trunk detection for a rubber-tapping robot. A tightly coupled sliding-window-based factor graph method is proposed for pose tracking, which introduces normal distribution transform (NDT) measurement factors, inertial measurement unit (IMU) pre-integration factors, and prior factors generated by sliding window marginalization. To actively pursue goals in navigation, a distance-adaptive Euclidean clustering method is utilized in conjunction with cylinder fitting and composite criteria screening to identify tree trunks. Additionally, a hybrid map navigation approach involving 3D point cloud map localization and 2D grid map planning is proposed to apply these methods to the robot. Experiments show that our pose-tracking approach obtains generally better performance in accuracy and robustness compared to existing methods. The precision of our trunk detection method is 93% and the recall is 87%. A practical validation is completed in robot rubber-tapping tasks of a real rubber plantation. The proposed method can guide the rubber-tapping robot in complex forest environments and improve efficiency.

In this work, we study, from a numerical point of view, the dynamics of a specific hybrid map which is that of the kicked van der Pol system. The dynamics of this system is generated by a two-stage procedure: the first stage is the time- τ map of the vector field associated with the van der Pol equation, and the second stage is a translation. We propose a numerical method for computing local (un)stable manifolds of a given fixed point, which leads to high order polynomial parameterization of the embedding. Such a representation of the dynamics on the manifold is obtained in terms of a simple conjugacy relation and by constructing two contracting operators. We illustrate our techniques by plotting (for specific values of the parameters) both stable and unstable manifolds displaying homoclinic intersection. Furthermore, our numerical study reveals the presence of a strange attractor included in the closure of the unstable manifold of the fixed point. [ABSTRACT FROM AUTHOR]

In this paper, an efficient chaos-based image encryption scheme for color images is proposed. This paper includes a strong displacement structure through a specified matrix scrambling method for pixel positions and a new hybrid chaotic map as a key generator for encryption. Chaotic Logistic map is used to generate binary bitstream for controlling diffusion process. The designed map exhibits superior performance in terms of key space range, complexity and chaotic substantiality that enhances security of the whole system. NPCR, UACI, MSE and PSNR values, which are frequently used in performance and security analysis in the field of image encryption, are at a satisfactory level and all parameters have successfully met the necessary conditions. For instance, in a good image encryption algorithm, ideal values of NPCR and UACI parameters should be 99.60% and 33.46%, respectively. The average results obtained in this study are very close to their ideal values as 99.6046 and 33.4733 for NPCR and UACI, respectively. According to a scientific study presented in the similar field, MSE value should be greater than 9,555; PSNR value must be less than 8.3875. In this study, average MSE and PSNR values calculated for six different test images are 10,315 and 7.9961, respectively. Some important analysis results have been compared to that of well-known Advanced Encryption Standard (AES) algorithm and a similar study presented in this field. Theoretical analysis and experimental results confirm that the proposed algorithm has great security and effectively encrypts and decrypts the color images with different sizes as well.

In this paper, an efficient chaos-based image encryption scheme for color images is proposed. This paper includes a strong displacement structure through a specified matrix scrambling method for pixel positions and a new hybrid chaotic map as a key generator for encryption. Chaotic Logistic map is used to generate binary bitstream for controlling diffusion process. The designed map exhibits superior performance in terms of key space range, complexity and chaotic substantiality that enhances security of the whole system. NPCR, UACI, MSE and PSNR values, which are frequently used in performance and security analysis in the field of image encryption, are at a satisfactory level and all parameters have successfully met the necessary conditions. For instance, in a good image encryption algorithm, ideal values of NPCR and UACI parameters should be 99.60% and 33.46%, respectively. The average results obtained in this study are very close to their ideal values as 99.6046 and 33.4733 for NPCR and UACI, respectively. According to a scientific study presented in the similar field, MSE value should be greater than 9,555; PSNR value must be less than 8.3875. In this study, average MSE and PSNR values calculated for six different test images are 10,315 and 7.9961, respectively. Some important analysis results have been compared to that of well-known Advanced Encryption Standard (AES) algorithm and a similar study presented in this field. Theoretical analysis and experimental results confirm that the proposed algorithm has great security and effectively encrypts and decrypts the color images with different sizes as well. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid chaotic map and a different way of using optimization technique to improve the performance of encryption algorithms. Compared to other chaotic functions, the proposed chaotic map establishes an excellent randomness performance and sensitivity. Based on its Lyapunov exponents and entropy measure, the characteristics of the new mathematical function are better than those of classical maps. We propose a new image cipher based on confusion/diffusion Shannon properties. The substitution phase of the proposed encryption algorithm, which depends on a new optimized substitution box, was carried out by chaotic Jaya optimization algorithm to generate S-boxes according to their nonlinearity score. The goal of the optimization process is to have a bijective matrix with high nonlinearity score. Furthermore, a dynamic key depending on the output of encrypted image is proposed. Security analysis indicates that the proposed encryption scheme can withstand different crypt analytics attacks. [ABSTRACT FROM AUTHOR]

In this paper, we propose an approach for cooperative mapping of traversable ground from aerial and ground views in structured outdoor and indoor environments. The presented approach achieves a hybrid map building based on traversable ground skeletonization and graph matching. The obtained map is an augmented ground traversability map, represented as a hybrid topological/metric graph from heterogeneous sources. This approach provides a very suitable representation for ground navigation and planning. To validate this approach, the proposed algorithm is applied between aerial views, provided by a UAV flying over an experimental site, and ground maps from ground robots at different exploration stages, in realistic simulation and real-world environments. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce the notion of common limit range property (CLR property) for two hybrid pairs of mappings in fuzzy metric spaces, and we prove common fixed point theorems using (CLR) property for these mappings with implicit relation. Our results extend some known results to multi-valued arena. Also, we prove common fixed point theorem in fuzzy metric spaces satisfying an integral type.

To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method.

• A novel strategy to fuse the camera and 2D-Lidar information for motion estimation. • A new strategy that utilizes both visual and Lidar data to determine the loop candidate. • Can obtain a hybrid metric-feature submap. • Can generate a global consistent metric-feature scene representation. • Experiments verify the mapping method is accurate and robust. Mapping the surroundings is a challenging task required for mobile robot. Fusion of heterogeneous sensors measurement has gradually been the main focus of robot perception. In this work, the Lidar and camera measurement is fused to obtain robust motion estimation and construct hybrid metric-feature map. By coupling visual information, the powerful tracking ability of visual features is heuristically applied to support the scan matching. With the estimation of Lidar odometry, the camera motion in complicated scene can be guaranteed. Meanwhile, the hybrid map is divided into submaps to improve the mapping efficiency, and each submap is composed of a grid-based metric submap and a group of visual features. Then a multi-stage loop closure strategy is applied to determine the loop candidate. Besides, multi-layer optimization including local bundle adjustment and pose graph optimization is performed to obtain global consistent map. The effectiveness of the proposed method is verified in different scenarios. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce the notion of hybrid tangential property for pairs of single-valued and multi-valued mappings in modified intuitionistic fuzzy metric spaces and we prove common fixed point theorems for such mappings in modified intuitionistic fuzzy metric spaces. [ABSTRACT FROM AUTHOR]

Abstract: In a mobile service robot, map building is a basis for navigation that allows the robot to perform a given task. When robots carry on mission planning and execution, they not only need structural information about the environment to navigation and self localization, but also need to possess a deeper knowledge to endow a robot with higher degrees of autonomy and intelligence. In this paper, the QR code based artificial labels are used to provide semantic concepts and relations of objects and rooms in indoor environments, which can solve the complexity and limitations of semantic recognition and scene understanding only with robot’s vision. Semantic knowledge offered by artificial labels is obtained to incarnate semantic representations of the environment, which are formed as the semantic map. In order to maintain the navigation abilities of the robot, the spatial modeling method of a human is imitated to describe the relations of function area as a global topological map by the spectrum clustering algorithm. The semantic map is combined with topological representations to build a spatial semantic hybrid map. Using the hybrid map, “semantic” path planning, the elementary management of objects, and the context based robot localization are studied to show the hybrid map’s practicability. The results of several experiments show that the spatial semantic hybrid map comes up to capture the human point-of-view of robot environments, enable a high-level reasonable service path, and achieve function-driven navigation. [Copyright &y& Elsevier]

This paper presents a new approach based on scan matching for global localization with a metric-topological hybrid world model. The proposed method aims to estimate relative pose to the most likely reference site by matching an input scan with reference scans, in which topological nodes are used as reference sites for pose hypotheses. In order to perform scan matching we apply the spectral scan matching (SSM) method that utilizes pairwise geometric relationships (PGR) formed by fully interconnected scan points. The SSM method allows the robot to achieve scan matching without using an initial alignment between two scans and geometric features such as corners, curves, or lines. The localization process is composed of two stages: coarse localization and fine localization. Coarse localization with 2D geometric histogram constructed from the PGR is fast, but not precise sufficiently. On the other hand, fine localization using the SSM method is comparatively slow, but more accurate. This coarse-to-fine framework reduces the computational cost, and makes the localization process reliable. The feasibility of the proposed methods is demonstrated by results of simulations and experiments. [ABSTRACT FROM AUTHOR]

To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents new object-spatial layout-route based hybrid map representation and global localization approaches using a stereo camera. By representing objects as high-level features in a map, a robot can deal more effectively with different contexts such as dynamic environments, human-robot interaction, and semantic information. However, the use of objects alone for map representation has inherent problems. For example, it is difficult to represent empty spaces for robot navigation, and objects are limited to readily recognizable things. One way to overcome these problems is to develop a hybrid map that includes objects and the spatial layout of a local space. The map developed in this research has a hybrid structure that combines a global topological map and a local hybrid map. The topological map represents the spatial relationships between local spaces. The local hybrid map combines the spatial layout of the local space with the objects found in that space. Based on the proposed map, we suggest a novel coarse-to-fine global localization method that uses object recognition, point cloud fitting and probabilistic scan matching. This approach can accurately estimate robot pose with respect to the correct local space. [ABSTRACT FROM AUTHOR]

To avoid the saturation of momentum wheels and the harm due to the thruster plume during the on-orbital manipulation, space robots usually stay in a free-floating state which follows the linear and angular momentum conservation leading to a kinematic coupling effect of the satellite base and the space manipulator. Emphasizing the stability of satellite base and execution safety, it is significant to minimize the kinematic coupling effect as well as avoid obstacles in the environment. Nevertheless, coupling minimization and obstacles avoidance are considered separately in previous work. By applying a hybrid map in the Configuration space, this article proposes a unified method dealing with the above two problems together. First, coupling factors are defined to evaluate the kinematic coupled effect which can be described by a coupling map; second, an obstruction map is generated by transforming obstacles in the Cartesian space to the Configuration space; the proposed hybrid map is finally generated from an overlay of a coupling map and an obstruction map. Numerical simulations verify the effectiveness of the method on a two degree-of-freedom planar space robot. [ABSTRACT FROM AUTHOR]