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Addressing the problems of the RIME in the mobile robot path planning problem, such as easy to fall into the local optimum and slow convergence speed, this paper proposed an enhanced rime optimization algorithm (ERIME) for the path planning of mobile robots in the complex environment. Firstly, this algorithm designed a lens imaging population selection strategy based on sine chaos mapping to improve the population initialization stage to increase the population diversity, so that the algorithm could be better explored and exploited. Secondly, this algorithm designed a stochastic factor-controlled optimal search strategy and a centroid-guided development mechanism to improve the exploration and exploitation stages of the algorithm, so as to enhance the algorithm's ability to escape from the local optimal solutions, better explore the global optimal solution, and accelerate the convergence speed of the algorithm. Additionally, this paper proposed a Markov chain model of the ERIME algorithm and proved the global convergence of the algorithm. To verify the effectiveness of ERIME, this paper validated the algorithm using the CEC2017 test set and compared it with other well-known meta-heuristic algorithms. The results show that the algorithm performs well. Finally, this paper applied the ERIME algorithm to the path planning problem of mobile robots in complex environments. The experimental results demonstrate that the proposed algorithm efficiently plans the robot's path and finds a high-quality route. [ABSTRACT FROM AUTHOR]

In view of the problem that it is difficult to reasonably plan the path after the mobile robot completes its self positioning and map construction, which leads to the disordered movement of the mobile robot and the waste of resources, ant colony algorithm is adopted to realize the path planning of mobile robot in this study. Ant colony algorithm is a probabilistic algorithm to solve the optimal path in a problem. However, in the general ant colony algorithm, all parameters of the ant colony algorithm are unchanged, resulting in the result of the ant colony algorithm de- pendent on the pheromone parameters set in the algorithm. In order to solve the above problems, the parameters of ant colony algorithm and pheromone allocation are improved, and the pheromone update standard is improved by changing the pheromone volatility coefficient and pheromone update standard in each iteration and combining with heuristic factors. Setting the adjustable pheromone volatile factor increases the adaptability of the algorithm. According to the meaningful parameter space, the path planning results of the traditional ant colony algorithm and the improved ant colony algorithm are compared under different environments. The path length of the improved ant colony algorithm is reduced by 4.48% and 8.54%, respectively, and no path crossover nodes are generated, which achieves the expected effect of reasonable path planning for mobile robots. [ABSTRACT FROM AUTHOR]

Patrolling is one of the basic tasks of robots which has a wide range of applications in reality. The related issues have received much attention in the research of computational geometry and robotics. The problem of robot patrolling requires continuous coverage of designated areas for a long time, and this paper employed idle time to express the patrol efficiency of robots. The shorter the idle time, the better the efficiency. Therefore, this paper designed a traversable circle model, where the robot needs to patrol the boundary and the diameter of the circle. This paper firstly proposed an algorithm which used two variable speed robots for this model. On the basis of analyzing the geometric features of the model, this paper proved the optimality of the algorithm according to its requirements. Then, this paper proposed an algorithm for three variable speed robots to cooperate with each other to patrol traversable circle and proved the optimal idle time of this algorithm is 19/25 of the partition algorithm. Finally, this paper verified the correctness of the theoretical analysis through simulation experiments. [ABSTRACT FROM AUTHOR]

Mobile robots operating in complex terrains demand superior traversability and adaptability. This study addresses this challenge by introducing a novel micro-tracked chassis design with improved terrain adaptability. The design utilizes track chassis as the bearing mechanism and optimizes its structure for improved performance. A combined approach involving theoretical analysis (e.g., mechanical calculations) and RecurDyn software simulations is employed to evaluate the cassis’s ability to traverse various obstacles, including longitudinal and transverse slopes, trenches, and vertical barriers. The analysis reveals key performance metrics, showing a maximum climbing angle of 35°, a maximum side slope of 30°, a maximum trench crossing width of 400 mm, and a maximum surmountable vertical obstacle height of 150 mm. Furthermore, simulations across different soil types (sand, clay, and heavy clay) indicate superior passability on sandy terrain. Additionally, a forwardshifted center of mass configuration improves obstacle crossing performance and driving stability. This research offers valuable insights and a theoretical foundation for the design of micro tracked chassis with exceptional off-road capabilities [ABSTRACT FROM AUTHOR]

For the traditional JPS algorithm in mobile robot path planning, there are path inflection points as well as too many intermediate jump points, and the path planning time is long. This paper proposed an improved jump point search (JPS) algorithm. The I-JPS algorithm eliminated redundant nodes and increased the safe distance between the robot and obstacles by improving the cost function and introducing the fork product formula. It also introduced DWA for local path planning for temporary obstacle avoidance and path smoothing by robots, and increased the priority of obstacle avoidance between multiple robots by improving DWA. Finally, it introduced multi-robot collaborative path planning, where multiple robots could work together and complete complex tasks, and the robots could also share information, coordinate their actions, and solve problems through division of labour to improve the efficiency of task completion. Finally, the experimental simulation results show that the improved algorithm is greatly improved in all aspects compared to the pre-improved one. [ABSTRACT FROM AUTHOR]

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Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The coverage tasks of mobile robots are evolving towards large-scale and intelligent di- rections demanding urgent requirements for the coverage efficiency and environmental adaptabil- ity of coverage path planning. To address the inadequate adaptability of the traditional boustro- phedon cellular decomposition in complex maps and to improve coverage efficiency, a complete coverage path planning method is proposed. Firstly, based on the boustrophedon cellular decom- position method. a strategy of area-descending traversal and monotone polygon judgment was proposed to merge cells, reducing approximately half of the cell quantity. Finally, by establis- hing the mapping relationship between the starting and ending positions of cells, a genetic algo- rithm was employed to optimize the selection of cells' starting positions and the global access or- der. The research results show that: 1) when processing maps with dimensions of 1 300 pixels. the algorithm in this paper can obtain calculation results within 10 s, and compared with the boustrophedon method, neural network method, and contour line method, the growth rate of calculation time is smaller with the increase of map area: 2) compared with the boustrophedon method, contour line method, neural network method, and energy-optimal method, the total op- eration time of robots in this paper is reduced by 5.4% to 47.0%, and the ineffective operation time is reduced by 5.8% to 29.2%; 3) the average coverage rate of this algorithm on 1 800 test maps reaches 99,91%; 4) the tests further confirm the significant coverage efficiency advantages of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

A mobile robot relocation algorithm based on ResNet was proposed to solve the problem of positioning loss caused by robot kidnapping and system failure restart. The proposed algorithm divides relocation into two stages: coarse matching based on ResNet and fine matching based on iterative closest point (ICP). In the coarse matching stage, the laser point cloud data is first converted into images, and then adjacent images in time are stacked into multi-channel images as input to ResNet to enhance the temporal features of the images. In the fine matching stage. the predicted position of the robot output by ResNet is used as the initial value of the ICP algorithm for point cloud fine matching, in order to obtain the final pose. For similar environments, a dynamic relocation method was proposed, which uses mobile robots to perform multiple relocations to avoid mismatches. Finally, a comparative experiment was conducted with the relocation algorithm augmented Monte Carlo localization (AMCL) in the simulation experiment. The localization time was reduced by 8.2 s and the localization success rate was improved by 43.4%, proving that the proposed algorithm has better relocation performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Autonomous navigation is the prerequisite and foundation for mobile robots to accomplish complex tasks. Traditional autonomous navigation systems rely on the accuracy of maps and cannot adapt to highly complex industrial and service scenarios. End-to-end navigation methods for mobile robots that do not rely on a priori map information and are able to make autonomous decisions through deep reinforcement learning, and environment interaction learning have become a new research hotspot. Most existing classifications cannot comprehensively summarize the challenges and opportunities of end-to-end navigation problems. Based on the characteristics of end-to-end navigation systems, the challenges of the navigation problem are attributed to the key issues of poor perception ability of navigation agents, ineffective learning and poor generalization ability of navigation strategies. The research status and development trends of end-to-end navigation systems are described. Representative research results in recent years addressing these key issues are detailed respectively, and their advantages and shortcomings are summarized. Finally, the future development trends of end-to-end navigation for mobile robots are prospectively envisioned in aspects such as visual language navigation, multi-agents collaborative navigation, end-to-end navigation for fusion super-resolution reconstructed images and interpretable end-to-end navigation, providing certain insights for the research and application of end-to-end navigation for mobile robots. [ABSTRACT FROM AUTHOR]

Objectives Localization is an important module of the light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) system, which provides basic information for perception, control, and planning, further assisting robots to accomplish higher-level tasks. However, LiDAR localization methods still face some problems: The localization accuracy and efficiency cannot meet the requirements of the robot products. In some textureless or large open environments, the lack of features easily leads to dangerous robot kidnappings. Consequently, aiming at the localization problems of mobile robots in large indoor environments, a global localization method based on cylindrical features is proposed.Methods First, an offline parameterized map is designed, which consists of some map cylinders and a raster map. Because the point cloud map contains a large number of 3D points and complete cylinders, random sample consensus (RANSAC) and geometric models are combined to directly segment the cylindrical points. The raster map is employed to describe the distributions of stable artificial structures. Then, some lightweight binary files are used to offline record the geometric model of cylinders and the feature distribution of the map. Next, based on three unique geometric characteristics of the cylinder (outlier, symmetry, and saliency), a real-time LiDAR point cloud cylinder segmentation method is proposed. Finally, two pose computation strategies are designed. The first is an optimization model based on heuristic search, which searches for the best matching cylinder between the map and real-time point cloud, and calculates the translation and rotation, respectively. The second is an optimization model based on multi-cylinder constraints, which employs both the topological relation (point-to-point and point-to-line constraints) and geometry attributes to find approximately congruent cylinders, then computes optimal pose.Results To verify the feasibility of the proposed method, we use a 16-line LiDAR to collect the experimental data in three real-world indoor environments, i.e., lobby, corridor, and hybrid scenarios. The global localization experiment is compared to a similar wall-based localization method, and the loop closure detection is compared to M2DP, ESF, Scan Context, and the wall-based localization. The experimental results show that the proposed method outperforms the baseline methods. The place recognition and localization performance of the proposed method reach the mainstream method level, with a localization success rate of 90% and an error of 0.073 m. Some data can reach millimeter localization accuracy, and the fastest speed is within 100 ms.Conclusions The proposed method can effectively realize the global localization and place recognition of the robots in typical open indoor environments. It meets the accuracy and efficiency requirements of autonomous driving for global localization in practical applications. It can be applied to solve the problems of position initialization, re-localization, and loop closure detection. [ABSTRACT FROM AUTHOR]

Objective In order to improve the adaptability of mobile robots to complex map environments, we propose a global path planning method called laser model, which improves the A* algorithm and ray model.Methods This method draws inspiration from the real-time scanning principle of planar light detection and ranging, which emits multiple virtual laser rays from the path node to the target point to perceive the boundary of obstacles, thereby quickly crossing obstacles and reaching the target point. At the same time, combined with the Floyd optimization algorithm, a safe, reliable, and stable global path is obtained in a relatively short time.Results In the concave environment of the experiment, the search time of the laser model is 99% faster than that of the A* algorithm and ray model, and the search process is reduced by 96%. In the experimental infeasible region, the laser model is 99% faster than the A* algorithm and ray model, and the search process is reduced by 97%. Compared with the double ant colony crossover algorithm, the laser model also performs better in concave environments.Conclusions The experimental results show that the proposed method is effective in solving the time consumption problems of concave traps and infeasible areas. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of trajectory tracking control of wheeled mobile robot (WMR), a backstepping optimized double sliding mode control strategy was proposed. Initially, based on the kinematics and dynamics model of WMR, the kinematic backstepping sliding mode controller and dynamic backstepping integral sliding mode controller are designed. The stability of the designed controllers is proved by Lyapunov theory. Then particle swarm optimization (PSO) algorithm is used to optimize the controller parameters. Finally, considering the external disturbance and system input saturation, Matlab/Simulink is used to verify the control strategy. The results show that the proposed control strategy not only ensures the robustness and real-time performance of the system, but also improves the control accuracy of the system. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots are prone to wheel slippage on soft and complex terrains, which affects trajectory tracking accuracy. To improve the trajectory tracking accuracy of robots under such conditions, an adaptive slip compensation controller (ASCC) based on fault‑tolerant control (FTC) is proposed. Firstly, a kinematic model of a four‑wheeled mobile robot (4 WMR) is established by considering wheel slippage rates, and the corresponding trajectory tracking error model is constructed. Then, based on the FTC method, a slippage compensation term is designed. The stability of the ASCC is proven using Lyapunov stability theory, achieving accurate compensation for the velocity error caused by wheel slippage. Finally, experiments under two working conditions are conducted on the 4 WMR. The results show that the designed controller enables the mobile robot to track the desired path under unknown wheel slippage rates, and it exhibits stronger robustness and higher tracking accuracy compared to the conventional controllers. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In some large buildings, the efficiency of path planning algorithms for mobile robots still faces great challenges. To deal with this problem, a path planning algorithm combining Voronoi region segmentation and path optimization (VSO) is proposed to achieve fast path planning in large-scale indoor scenario. The algorithm uses the generalized Voronoi graph (GVG) to construct the topological map from the environmental map, and then the initial heuristic path can be obtained quickly on the topological map. By sampling in the area around the initial path, the over-exploration of the workspace can be reduced. What's more, the algorithm selects waypoints to divide the sampling area into multiple sub-regions, and search paths in the sub-regions in parallel to further reduce the search space and improve the search efficiency. Finally, the path obtained from each sub-region is connected as the solution of the overall problem, and the optimization algorithm is used to smooth the final path. Simulation and robot experiments are conducted to verify the practicability and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem of optimal path planning for forest fire prevention mobile robots in forest terrain conditions, an improved ant colony algorithm based on extended neighborhood was proposed. Firstly, the plan of directional extended neighborhood was introduced, and the search neighborhood was extended from 8 to 10, which was in an attempt to expand search efficiency and scope. Then, considering multiple factors that affect the mobile robot, path length and energy consumption were used to improve heuristic functions. Next, the initial pheromone was improved by incorporating location information. Finally, the advantages of the Max-Min Ant System (MMAS) and the elite ant model were combined to improve the pheromone updating rule. Results indicated that the proposed improved algorithm compared with traditional ant colony algorithm and the ant colony algorithm based on multiple inspired factor, which can shorten the path length by 7. 66% and 6. 53%, reduce energy consumption by 62. 2% and 49. 3%, decrease the comprehensive index by 32. 6% and 23. 1%. The research demonstrated that the proposed improved ant colony algorithm had stronger global search capabilities and considerable practical value [ABSTRACT FROM AUTHOR]

In response to the issues of a wide variety of equipment, large batches, heavy payloads, limited space, complex assembly paths, and high assembly risks inside the cabin, a heavy-duty positioning and assembly robot was designed. Based on the study of the robot kinematics, an error model was established. With the radius of minimum bounding sphere as the constraint condition, the identification results of error parameters by genetic algorithm were compensated into the robot control system. Taking cabinet assembly as an example, a working path was planned based on spatial constraint conditions. By a dynamic constraint energy consumption function model, multi-objective optimal trajectories were obtained with time, impact, and energy consumption as optimization objectives. Prototype testing verified the effectiveness of the error parameter identification, which reduces the absolute positioning errors of the robot. Moreover, the multi-objective optimal trajectory has a small total joint impact and smooth motion, achieving efficient, smooth, and reliable installation of cabinet-type equipment. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing local path planning algorithms only achieve free movement of mobile robots in the scenario. But local path generation does not consider road constraints in the scenario, which is not applicable to some regularized structured roads. The OpenPlanner algorithm solves this problem well. But the local path planned by the traditional OpenPlanner algorithm does not meet the maximum turning curvature constraint of the mobile robot and cannot be effectively tracked by the mobile robot. In order to solve the above problem, the traditional OpenPlanner algorithm is improved from two aspects: state sampling and evaluation function. The improved OpenPlanner algorithm is applied to local path planning of mobile robots. In the state sampling stage, the optimal local path solution space is expanded by designing a double-layer local path cluster. The longitudinal sampling distance of the first layer local path cluster is linearly related to the driving speed in sections. The longitudinal sampling distance of the second layer local path cluster is 1.5 times that of the first layer local path cluster. In the path selection stage, the curvature cost of the path (obtained by summing the curvatures of each sampling point on the local path) is introduced into the evaluation function to ensure that the local path cluster satisfies the maximum turning curvature constraint of the mobile robot, thereby making the local path tracked by the mobile robot. The experimental results show that compared with the traditional OpenPlanner algorithm, the improved OpenPlanner algorithm filters the optimal local path with smoother turning. The average curvature is reduced by 31.3% and 6.2% in obstacle free and obstacle present scenarios, respectively. Moreover, the local path can be well tracked by mobile robots.

Aiming at the problems of error compensation after robot error modeling, an improved algorithm was proposed based on pseudo target iteration. This algorithm generated a new pseudo target in each iteration to correct the joint angles, thereby continuously reducing the actual errors of the robot. The pseudo target iterative algorithmic process was improved to mature the algorithm flow, 5 new and different false target generation methods were proposed, and the characteristics and applicable scenarios of each method were analyzed. An ensemble algorithm was proposed combining multiple false target generation methods to further improve the accuracy of error compensation. Using the HSR-JR612 robot to carry out simulation experiments, the simulation results show that the algorithm takes milliseconds and the compensation effectiveness is good, and the integrated algorithm may further improve the pose compensation effect. Finally, the UR10 robot and the laser tracker were used for experiments. The experimental results show that after compensation, the position errors of the or bot end may be reduced to less than 0.06 mm, and the attitude errors may be reduced to less than 0.025°. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, an improved artificial potential field method is proposed to address target unreachability and local minima in traditional artificial potential field method. First, the distance factor between the robot and the target point is introduced into the repulsive field function to solve the problem of unreachable target. Second, an elliptical target strategy is proposed to guide the robot to escape from local minima by setting virtual target points, on the basis of which an elliptical tracking strategy is proposed to escape from local minima by tracking a partially established ellipse to reach a virtual target point. Finally, to meet the continuity requirement of the robot velocity and acceleration, the planned path is fitted with a cubic uniform B spline curve. The experimental results show the improved artificial potential field method effectively remedies the problems of traditional artificial potential field method. The elliptical tracking strategy requires a shorter path length than the elliptical target strategy and after the sample curve processing, the path is smoother compared with the original one. [ABSTRACT FROM AUTHOR]

For the formation motion of multiple wheeled mobile robots in a narrow road section, we investigate the formation control design problem for multiple wheeled mobile robots with output constraints in the framework of the leader-follower method. The proposed control strategy can guarantee that both the leading and following wheeled robots move along an identical target trajectory curve and maintain a certain distance under the influence of external disturbances. For the leading wheeled robot, a controller based on a dynamic tracking target is designed, which transforms the original tracking control problem into curvature tracking. As for te follower, a robust controller is designed based on the geometrical relationship between the leading and following robots during the motion process, by the virtue of the virtual structure method and the Barrier Lyapunov function method. This can ensure that the following error is limited to a certain range and all the following wheeled robots perform the formation motion task accurately. In the end, numerical simulation results demonstrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing multi-agent network researches ignore the matching between qualities of local networks and the requirements of regional tasks, which influences task completion efficiency and quality. In view of this, this paper proposed a network connectivity quality control method based on matching task requirement. Firstly, this method analyzed the intermittent connectivity of multi-agent network by using states of agents. Secondly, it constructed a subnet set based on leader-follower model to execute tasks, according to the requirements of tasks and the intermittent connectivity of network, and it further evaluated the connectivity quality of the subnet set. Finally, this method designed a multi-agent mobility optimization model based on network connectivity quality, in which the maximum connectivity quality of subnet set represented to the network connectivity quality. Under the constraints of agent’s mobile distance and network connectivity quality, this model obtained multi-agent mobility strategy with optimal task completion rate. Experimental results verify that the proposed method can effectively control the network connectivity quality, maintain the task completion rate and improve the mobile utility of agents. [ABSTRACT FROM AUTHOR]

Continuous, stable and accurate jumping in three-dimensional (3D) environment is the research difficulty of monopod jumping robot. A monopod jumping robot with the center of mass that can accurately reach a target position point in 3D space was designed. Based on the spring loaded inverted pendulum (SLIP) dynamics model, the control of robot take-off speed was realized, making the controllable continuous bounce possible. Based on the reaction wheel pendulum (RWP) dynamics model and the law of conservation of angular momentum, the real-time 3D attitude control of robot with three inertial tails was realized. By integrating SLIP model, RWP model and the proposed post-landing steering strategy, a continuous jumping algorithm for the center of mass of robot to accurately reach the expected 3D target point was designed. The simulation results show that the monopod robot can achieve continuous, stable and accurate jumping in 3D environment, which verifies the feasibility of the proposed 3D jump dynamics model and its control algorithm. [ABSTRACT FROM AUTHOR]

An improved Informed-RRT* path planning algorithm based on node optimization is proposed to address the characteristics of the current Informed-RRT* algorithm that is slow, poorly purposeful and unsmooth in path planning. The adaptive t-distribution function is introduced to change the distribution probability of random points in different environments to improve the efficiency of the algorithm. Then an elliptic focal bias strategy is used to extend a single bias point to the whole elliptic focal point to make the random tree grow close to the minimum distance between the starting point and the target point, which increases the purposefulness of the algorithm. Finally, the re-election grandparent node strategy is used to reduce redundancy for the whole path, and the symmetric multipole type curve method is used to smooth for the path turns. The improved Informed-RRT* algorithm has higher search efficiency, more purposefulness, and smoother planned paths by comparing the surface with multiple sets of experiments. [ABSTRACT FROM AUTHOR]

Aiming at the problem of overestimation, low sample utilization and sparse reward of DQN algorithm in mobile robot path planning, an end- to- end path planning method based on improved deep reinforcement learning is proposed, namely the curiosity distillation module dueling deep double Q-network prioritized experience replay (CDM-D3QN-PER). This method is based on D3QN to reduce the adverse effects of overestimation. Long short term memory (LSTM) is added to the input to process the information of radar and camera to obtain more favorable environmental information. It uses prioritized experience replay (PER) as sampling method to make full use of samples and improve sample utilization, and the curiosity distillation module (CDM) is introduced to alleviate the problem of reward sparsity to some extent. The experimental results show that compared with DQN, DDQN and D3QN, the number of robots reaching the target point trained by CDM-D3QN-PER algorithm is significantly increased, and it is three times that of DQN algorithm. The algorithm makes reward worthy of promotion, network convergence speed is improved, in unknown complex environment can better obtain the optimal path. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, an autonomous mobile feeding robot system for eel farming is developed, which mainly includes AGV handling trolley, XYZ CNC electric synchronous belt sliding table linear guide module, feeding peristaltic pump, motion controller, etc. The AGV handling trolley detects the guiding magnetic stripe in real time to determine the working path through the sensing device. The motion controller outputs pulse signals to the stepping motor of the XYZ three-dimensional CNC electric synchronous belt slide table linear guide module to achieve the spatial positioning of the robot feeding position. By controlling the rotational speed of the peristaltic pump drive motor, the precise feeding of bait and the stability of the feeding process are realised. Nine prototype performance tests were carried out for the three-layer three-dimensional eel pond with a target feeding volume of 9.5 g. The results showed that the average error of the robot spatial positioning accuracy was （2.1, 1.8, 2.1） mm, and the standard deviation was （2.6, 2.3, 2.5） mm, and the average feeding rate of the robot was 1.85 g/s, and the average feeding volume was 9.3 g, with an error of less than 5%. To assess the stability and reliability of the system, the robot was operated continuously for 50 hours at a travelling speed of 0.5 km/h without technical failures or system interruptions. In conclusion, the autonomous mobile feeding robot is stable and reliable, with high feeding accuracy and good feeding effect, which has strong practical and popularisation value. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of low accuracy and poor robustness of Visual Simultaneous Localization and Mapping (VSLAM） in dynamic environment, This paper proposed an adaptive Dynamic SLAM（AD-SLAM）. The scene dynamic classification front-end based on epipolar geometry constraint perceives the dynamic changes of the working environment in real time. The dynamic feature culling in low dynamic environment based on geometric constraints and moving probability and the dynamic target processing in high dynamic environment based on semantic segmentation eliminates moving objects. Experimental verification on the computer vision dataset TUM shows that the proposed method can improve the accuracy and robustness of localization in the mapping process of mobile robots while ensuring the real-time performance of the algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)