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In the smart home environment, aiming at the disordered and multiple destinations path planning, the sequencing rule is proposed to determine the order of destinations. Within each branching process, the initial feasible path set is generated according to the law of attractive destination. A sinusoidal adaptive genetic algorithm is adopted. It can...
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While solving the optimization problems of complex functions, particle swarm optimization (PSO) would be easy to fall into trap in the local optimum. Besides that, it has slow convergence speed and poor accuracy during the late evolutionary period. So a SA-PSO algorithm would be proposed in this paper. Classically, the probability to accept bad sol...
Citations
... In [28], from the system point of view, the author reviewed the latest work of underwater robots supporting IPMC from the perspective of modeling, manufacturing and biologically inspired design. In [29][30][31], the author's research results demonstrate that cultural genetic algorithms can effectively solve delays and problems, avoid falling into a peak and guide the mobile robot more effectively. ...
The rapid development of artificial intelligence and robots, the research and development of intelligent dialogue robots are very necessary for today's society. However, a robotic algorithm system that simulates navigational English conversation has not yet been developed. In order to find a suitable algorithm system for dialogue robots, this paper uses the test data set to test the analytical model of navigational English dialogue instructions. The experimental results show that the conditional random field (CRF) + domain dictionary + ambiguity resolution method has the highest segmentation effect. The calculated percentages of the analytical model are correct rate: 76.85%; recall rate: 80.36%; F-value: 88.46%. This paper implements a robot teaching and reproduction method based on simulated navigational English conversation and human-computer interaction under the background of artificial intelligence, and designs robot motion realization experiments and speech recognition experiments. The three-dimensional error after fine-tuning the voice is between 1.6798mm and 2.9968mm. This article constructs a simulation navigational English dialogue robot system. The FAQ component has up to 79.2%; others have a lower accuracy rate of only 59.03%.
... From the cultural genetic algorithm, techniques of reducing turns with the help of parallelogram and triangles are used to reduce the length in the belief space and increase the population quality. The fallback technique can be used to escape the "U" trap effectively [26]. This method reduces the collision in a dynamic environment with obstacles. ...
Autonomous mobile robots are getting more important because they can make decisions on their own in the given environment. The robot path planning is an important function in the autonomous robot. In this review, several path planning techniques are investigated to analyze the performance. The path planning techniques are categorized as static and dynamic. A Q-learning method with flower pollination algorithm and the genetic algorithm has an effective performance on the path planning of mobile robot (MR) in a dynamic environment. The ant colony optimization (ACO) method shows considerable performance in the static environment. The aerial vehicle path planning method also reviewed and the method based on geometric random tree shows the effective performance in path planning. The comparison table shows the latest research in the MR path planning with the advantages, limitations, and performance analysis.
... The problem arises when the graph contains more than one destination node [5]. The set of destination nodes is a subset of all network nodes and it becomes a multi-destination shortest path problem [6], which has many real-life applications, such as finding the shortest path among multi-destination items in supermarkets, warehouses, libraries, road networks [7], robotics industries [8], service compositions [9,10], and multicast routes [11]. The different techniques found in the literature are calculating either a multi-destination or single-pair shortest path to solve these issues [12]. ...
The shortest path-searching with the minimal weight for multiple destinations is a crucial need in an indoor applications, especially in supermarkets, warehouses, libraries, etc. However, when it is used for multiple item searches, its weight becomes higher as it searches only the shortest path between the single sources to each destination item separately. If the conventional Dijkstra algorithm is modified to multi-destination mode then the weight is decreased, but the output path is not considered as the real shortest path among multiple destinations items. Our proposed algorithm is more efficient for finding the shortest path among multiple destination items with minimum weight, compared to the single source single destination and modified multi-destinations of Dijkstra’s algorithm. In this research, our proposed method has been validated by real-world data as well as by simulated random solutions. Our advancement is more applicable in indoor environment applications based on multiple items or destinations searching.
... This can be of three types. a) Each of the targets is dependent on the other targets, b) The targets are independent of each other, c) Combination of type-a and type-b (Dong et al., 2016) i.e. some of the targets are dependent and rest are independent. At first, a Domain Knowledge Based Genetic Algorithm for Single Target has been proposed to deal with Robot's path planning problem. ...
In path planning problem of a mobile robot, the objective is to find an optimal collision free path from a source to single or multiple targets. Domain knowledge based genetic algorithms have been proposed to address the path planning problem having single as well as multiple independent targets. Four new domain knowledge based operators namely “circuit removal operator”, “insertion-deletion operator”, “refinement Operator”, and “target alignment operator” have been introduced in this work. Among these four operators, first three have been used for the path planning problem having single target, whereas all these four operators have been used for path planning problem having multiple independent targets. Proposed methods have been deployed on several simulated environments of varying sizes. From the experimental results, it has been established that the domain knowledge based operators enhance the capability of conventional genetic algorithm. Our proposed method for Mobile Robot’s Path Planning Problem having single target has outperformed some previously proposed evolutionary algorithm based methods.
... These details are used for the path planning [1]. The goal of path planning is to determine first a path and then the shortest or optimal path of the mobile robot from current location to destination within a static or dynamic obstacle environment [2] [3] [4]. Several sensors as laser sensors, ultrasonic sensors and stereo-camera-based range sensors are commonly utilized to detect obstacles in natural environments. ...
The robotic challeng of developing and implementing a shortest-path finding algorithm to reach a destination without bumping into obstacles has been tackled in this paper. The proposed algorithm is utilized a single ceiling fixed camera and based on the mask and robot size with different sizes for dilation mask. The implementation has been successfully produced thirty-three computer vision figures and two comparison tables of simulation results by using morphological structuring element with different sizes. This paper's proposal of optimal path finding algorithm is effeciently save robot energy in reaching targets.
... These details are used for the path planning. The goal of path planning is to determine first a path and then the shortest or optimal path of the mobile robot from current location to destination within an static or dynamic obstacle environment [1] [2] [3]. Several sensors as laser sensors, ultrasonic sensors and stereo-camerabased range sensors are commonly utilized to detect obstacles in natural environments. ...
Vision-based robot navigation is a key goal for a long time in most research for both robotics and computer vision. The challenge is how a mobile robot to navigate well to a goal whereas avoiding both static and dynamic obstacles. This paper will supply a unique process to compute the shortest path in an indoor environment for a mobile robot to ease its movement. The proposed way also detects and avoids obstacles in an environment using a single ceiling fixed camera. The proposed way creates a path for a known source that represents the current location of the robot, and destination. The path details are transmitted to the robot which travels using those details. This paper investigates the path creating by computer vision and internal processing, not outer communication mechanism. This paper has great importance because of its fast execution speed also vision is a smart sensor as it helps in the design of economically possible systems with simpler sensor restrictions. Vision sensor provides information about the environment that is unreachable to other sensors. Keywords-robot; computer vision; shortest path and single camera.
... For the problem of robot's path planning, many scholars have studied it. Par-How to cite this paper: Li, Z.P. and Li, X.T. ticle swarm optimization algorithm or navigation algorithm are used to solve the problem [3] [4] [5] [6] [7]. In the aspect of robot's task allocation, task allocation strategy [8] and ant colony algorithm are adopted [9] [10]. ...
The emergence of intelligent mobile robots has liberated the human labor to a certain extent, especially their abilities to work in harsh environments in place of humans. For intelligent mobile robots, how to achieve fast path optimization is an important issue. In this article, the model establishment method of environmental information collected by robot sensors and the genetic algorithm for real-time optimization of running paths are briefly introduced first, the crossover, mutation probability, and fitness function are improved based on the shortcomings of the traditional genetic algorithm, and then the simulation analysis of the two algorithms is carried out using matrix laboratory (MATLAB) software. The results show that the improved algorithm obtains a smaller length of optimal path, fewer inflection points, and a smaller turning angle, which also converges faster and has a greater degree of fitness. It takes 0.053 s for the traditional algorithm to calculate the optimal path, while the improved algorithm needs 0.013 s. In summary, the improved genetic algorithm can quickly and efficiently calculate the optimal path, which is suitable for real-time path optimization of mobile robots.
The shortest path problem is the problem of finding a path with minimum total weight from a source node to each destination node in a network. The existing solution to this fundamental problem searches the shortest paths to all network nodes until it meets the given multiple-destination nodes. By granting preference to routes to each destination node, the proposed algorithm meets the destination nodes faster. The results of the experimental analysis on a real-world dataset and simulated random networks show the superiority of the proposed algorithm to the existing solution. This remarkable improvement makes the proposed algorithm applicable in all related applications.
