Algorithm For Robot Path Planning Research Articles

Robot Path Planning and Tracking with the Flower Pollination SearchOptimization Algorithm

Robot path planning and tracking involve two critical aspects of autonomous navigation systems: determining an optimal route for the robot to follow and ensuring that it accurately adheres to this path in real-time. Path planning focuses on generating a feasible and efficient trajectory from a starting point to a destination while considering obstacles, dynamic environments, and constraints. This paper investigates the effectiveness of the Flower Pollination Search Optimization (FPSO) algorithm for robot path planning and compares its performance with traditional algorithms such as A* Algorithm, Dijkstra, and Rapidlyexploring Random Tree (RRT). The FPSO algorithm was evaluated across three distinct scenarios, demonstrating superior performance in terms of path length, computation time, and path smoothness. In Scenario 1, FPSO achieved an optimized path length of 10.5 meters with a computation time of 3.2 seconds, a path smoothness score of 8.9, and a path efficiency of 95%. In Scenario 2, FPSO resulted in a path length of 12.3 meters and a computation time of 3.5 seconds, with a smoothness score of 8.7 and an efficiency of 93%. Scenario 3 showed FPSO's best performance, with a path length of 9.8 meters, computation time of 3.0 seconds, a smoothness score of 9.0, and a path efficiency of 96%. Comparatively, the A* Algorithm, Dijkstra, and RRT exhibited longer path lengths and higher computation times, with lower smoothness scores and efficiencies.

An improved genetic algorithm for robot path planning

The conventional genetic algorithm (GA) for path planning exists several drawbacks, such as uncertainty in the direction of robot movement, circuitous routes, low convergence rates, and prolonged search time. To solve these problems, this study introduces an improved GA-based path-planning algorithm that adopts adaptive regulation of crossover and mutation probabilities. This algorithm uses a hybrid selection strategy that merges elite, tournament, and roulette wheel selection methods. An adaptive approach is implemented to control the speed of population evolution through crossover and mutation. Combining with a local search operation enhances the optimization capability of the algorithm. The proposed algorithm was compared with the traditional GA through simulations, demonstrating shorter path lengths and reduced search times.

Mobile Robot Path Planning Algorithm Based on NSGA-II

Path planning for mobile robots is a key technology in robotics. To address the issues of local optima trapping and non-smooth paths in mobile robot path planning, a novel algorithm based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is proposed. The algorithm utilizes a search window approach for population initialization, which improves the quality of the initial population. An innovative fitness function is designed as the objective function for optimization iterations. A probability-based selection strategy is employed for population selection and optimization, enhancing the algorithm’s ability to escape local minima and preventing premature convergence to suboptimal solutions. Furthermore, a path smoothing algorithm is developed by incorporating Bézier curves. By connecting multiple segments of Bézier curves, the problem of the high computational complexity associated with high-degree Bézier curves is addressed, while simultaneously achieving smooth paths. Simulation results demonstrated that the proposed path planning algorithm exhibited fewer iterations, superior path quality, and path smoothness. Compared to other methods, the proposed approach demonstrated better overall performance and practical applicability.

Expected-mean gamma-incremental reinforcement learning algorithm for robot path planning

Recently, researchers have been extensively exploring the immense potential of Q-Star. However, the available resources lack comprehensive information on this topic. Despite this, a Q-table, an uncomplicated lookup table containing actions and states, is often seen as a mere data structure for tracking. This overlooks the vast amount of knowledge that can be derived from it through visualization. The Q-learning algorithm utilizes this table to update values and determine the highest anticipated rewards for actions in each state. However, instead of relying solely on complex reward functions for algorithm development, leveraging the existing knowledge within the Q-table would be highly beneficial. Incorporating this valuable information into the algorithmic framework can minimize the need to develop intricate reward functions. This paper proposes an expected-mean gamma-incremental Q approach to tackle the challenges of convergence speed in an uninformed search reinforcement learning (RL) algorithm and the issue of path optimality in path planning problems. The gamma-incremental RL method revolves around adjusting the weight of the future value by considering the level of exploration. It enables the robot to receive preference feedback, either near-term reward or long-term reward, based on the frequency of the visited state. Meanwhile, the expected-mean technique uses the information of the robot's turning actions to update the Q-target. By consistently incorporating valuable insights from the Q-table, the algorithm can gradually enhance its understanding of the available information, resulting in more efficient decision-making. The experiment results indicate that the proposed algorithm accelerates the convergence rate, outperforming the baseline Q-learning by up to 2 times. It addresses the challenge of robot path planning by prioritizing promising solutions, resulting in near-optimal outcomes with higher total rewards and enhanced learning stability.

Study of Robot Path Planning with Improved A* And DWA Algorithm Fusion

For some problems of the traditional A* algorithm in robot path planning, such as inefficient search, many and complex inflection points in the designed and realized paths, as well as the inability to complete real-time dynamic path planning for obstacle avoidance, this paper proposes an improved A algorithm and combines it with Floyd for path rounding, and finally with DWA (Dynamic Window Approach) algorithm to be Fusion. In the improved A* algorithm, a carefully normalized random obstacle environment is used and the search process is optimized by reducing the search direction and redundant nodes. Meanwhile, the evaluation function is adjusted to achieve the optimal global design solution, and path optimization and obstacle avoidance are performed by the DWA algorithm. The experimental results finally show that when the robot is in a complex environment, the fusion algorithm can ensure the completion of the globally optimal route while avoiding obstacles in a timely and effective manner, which realizes more efficient path planning.

Enhanced Robot Motion Block of A-Star Algorithm for Robotic Path Planning.

An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.

Enhancing a star algorithm for robot path planning

This paper describes the importance of robot path planning in artificial intelligence and control theory, and proposes three improvements to the A-algorithm: bi-directional A-search, improved heuristic functions and pruning strategies. The performance of the different algorithms in terms of computation time, path length and number of nodes is compared through experiments. Moreover, it is emphasised in the article that in practical applications suitable algorithms and their improvements are selected according to the characteristics of the specific problem and reasonable evaluation criteria are used to measure the performance of the algorithms.

A hybrid strategy-based GJO algorithm for robot path planning

Addressing the challenges of low convergence accuracy and stagnation at local optima in the application of the golden jackal optimizer (GJO) to mobile robot path planning, this paper proposes a hybrid strategy-based golden jackal optimizer (HGJO) algorithm. The improved algorithm employs a pre-decreasing slow nonlinear energy decay strategy to balance the global and local search capabilities. The roulette wheel selection algorithm and Lévy flight strategy are introduced into the position update of the GJO algorithm, so the proposed algorithm avoids stagnation at the local optimum. The HGJO algorithm is evaluated against some state-of-the-art optimizers on 23 benchmark functions and the CEC2021 benchmark function. It is also applied to ablation experiments for mobile robot path planning. The experimental results show that the HGJO algorithm improves the average path length in path planning by 0.21%, 82.4%, and 7.9% over the original algorithm in three different environments under 30 independent experiments.

A self-learning Monte Carlo tree search algorithm for robot path planning.

This paper proposes a self-learning Monte Carlo tree search algorithm (SL-MCTS), which has the ability to continuously improve its problem-solving ability in single-player scenarios. SL-MCTS combines the MCTS algorithm with a two-branch neural network (PV-Network). The MCTS architecture can balance the search for exploration and exploitation. PV-Network replaces the rollout process of MCTS and predicts the promising search direction and the value of nodes, which increases the MCTS convergence speed and search efficiency. The paper proposes an effective method to assess the trajectory of the current model during the self-learning process by comparing the performance of the current model with that of its best-performing historical model. Additionally, this method can encourage SL-MCTS to generate optimal solutions during the self-learning process. We evaluate the performance of SL-MCTS on the robot path planning scenario. The experimental results show that the performance of SL-MCTS is far superior to the traditional MCTS and single-player MCTS algorithms in terms of path quality and time consumption, especially its time consumption is half less than that of the traditional MCTS algorithms. SL-MCTS also performs comparably to other iterative-based search algorithms designed specifically for path planning tasks.

Using neural networks to explore path planning algorithms for robots

In the introduction section of this paper, the importance of path planning for automatic self-navigation is outlined, along with the structure and basic model of neural networks, as well as numerous practical examples of using neural networks to solve the problem of traffic prediction and autonomous robot navigation. In the method section, the specific formulations and steps of how neural networks can be used to solve the path planning problem are explained in detail. Additionally, a variety of algorithms for solving the path planning problem with neural networks are introduced, and practical experiments are conducted to compare the results of these algorithms in various environments. In the RESULTS section, the characteristics are summarized and explained by comparing the experimental results. In the CONFUSION section, a fundamental outlook on the future of neural networks for solving path-planning problems is provided, as well as the possibility of cooperation between neural networks and other methods for solving path-planning problems.

Mobile Robot Path Planning Algorithm Based on RRT_Connect

Targeting some problems of the RRT_Connect path planning algorithm, such as average search and low efficiency, proposes an improved RRT_Connect algorithm that may optimize the searched nodes and parts of planned paths. Firstly, an improved RRT_Connect algorithm based on destination and searched node bias strategy is proposed. Secondly, an improved RRT_Connect algorithm is put forward for the optimization of the searched nodes and some planned paths to deal with the problem of low quality reflected in the improved RRT_Connect path planning algorithm, and the optimization for the cost of path planning by figuring out valid new nodes and parent nodes of adjacent nodes within a certain range. On this basis, the path planning algorithm is verified by simulation and actual experiments. It is shown by the experimental results that the improved RRT_Connect algorithm proposed in this paper can not only shorten the time and length of path planning but also decrease the number of search iterations and nodes.

Multi-Robot Distributed Communication in Heterogeneous Robotic Systems on 5G Networking

This work aims at new opportunities for the development of the robot communication network. Besides, a robot operation and decision control method driven by Digital Twins (DT) is designed to utilize lasers in a virtual environment. The multi-agent is integrated with the Proximal Policy Optimization (PPO) algorithm, forming the Muti-PPO algorithm for robot path planning. In addition, a consistent hashing algorithm is introduced and optimized as a Weighted Consistent Hash of Multiple Mappings (WCHMM) algorithm with multiple mappings. The experimental results indicate that the average task execution time of the WCHMM algorithm in the case of three bus nodes is 8.09s, which is 32.47 percent and 21.38 percent lower than the task execution time in the case of a single bus node and two bus nodes, respectively. The multi-robot distributed communication system based on Service Oriented Architecture (SOA) gives full play to the various inherent advantages of the multi-robot system.

Robot path planning based on improved genetic algorithm

In order to solve the problems of the basic genetic algorithm in robot path planning, such as the path is not smooth enough, the number of turns is too many, and it is easy to fall into the local optimal solution, an improved genetic algorithm is proposed. The method introduces the turning angle and turning times as the evaluation objectives, improving the practicability of the fitness function; The elitist retention strategy is added to improve the convergence speed of the algorithm; The adaptive change strategy of crossover probability and mutation probability is optimized, so that the algorithm can adapt to various scenarios and improve the optimization ability and convergence speed of the algorithm. The simulation results show that the improved genetic algorithm is more suitable for robot movement than the basic genetic algorithm.

An improved RRT* algorithm for robot path planning based on path expansion heuristic sampling

Rapidly-exploring Random Tree Star (RRT*) algorithm and its variants based on random sampling can provide a collision-free and asymptotic optimal solution for many path planning problems. However, many RRT* based variants have low sampling efficiency and slow convergence rate in the environment which consists of long corridors, due to a large number of iterations are required in sampling critical nodes. To overcome this problem, the paper proposes the Expanding Path RRT* (EP-RRT*) based on heuristic sampling in path expansion area. By combining the greedy heuristic of Rapidly exploring Random Tree (RRT)-Connect, EP-RRT* quickly explores the environment in order to find a feasible path, and then expands it to obtain the heuristic sampling area. It iteratively searches in the heuristic sampling area which also changes with the continuous optimization of the path, and finally obtains an optimal or suboptimal path connecting starting point and target point. Comparisons of EP-RRT* with RRT* and Informed RRT* in four simulation environments verify that EP-RRT* improves the node utilization, accelerates the convergence rate, and obtains a better path for the same number of iterations.

Conflict-based search with D* lite algorithm for robot path planning in unknown dynamic environments

This study proposes a locally observable robot pathfinding algorithm, conflict-based search with D* lite (CBS-D*), to realize automatic and effective pathfinding in mixed environments with dynamic obstacles. This algorithm takes Manhattan distance as the heuristic function and extends the incremental search range from 1order to 3order neighbors. It presents a prejudgment mechanism of collision avoidance and investigates a wait and circuity strategy to promote pathfinding performance. Compared with the D* lite, the experimental results demonstrate that CBS-D* achieves a higher success rate and obstacle avoidance number, and a lower time step. By this collision avoidance mechanism, CBS-D* gives all successes in pathfinding in various dynamic environments, while D* lite may result in some failures. Specifically, CBS-D* has around 31% in the average success rate of pathfinding improved to D* lite in a 32 × 32 map. Furthermore, CBS-D* gives a superiority of self-adaptability and intelligence in unknown dynamic environments.

A modified Q-learning algorithm for robot path planning in a digital twin assembly system

Product assembly is an important stage in complex product manufacturing. How to intelligently plan the assembly process based on dynamic product and environment information has become a pressing issue that needs to be addressed. For this reason, this research has constructed a digital twin assembly system, including virtual and real interactive feedback, data fusion analysis, and decision-making iterative optimization modules. In the virtual space, a modified Q-learning algorithm is proposed to solve the path planning problem in product assembly. The proposed algorithm speeds up the convergence speed by adding a dynamic reward function, optimizes the initial Q table by introducing knowledge and experience through the case-based reasoning algorithm, and prevents entry into the trapped area through the obstacle avoiding method. Finally, the six-joint robot UR10 is taken as an example to verify the performance of the algorithm in the three-dimensional pathfinding space. The experimental results show that the performance of the modified Q-learning algorithm is significantly better than the original Q-learning algorithm in both convergence efficiency and the optimization effect.

A Mobile Robot Path Planning Algorithm Based on Improved A* Algorithm and Dynamic Window Approach

The traditional A* algorithm has several problems in practical applications, such as many path turning points, redundant nodes, and long running time. it is sometimes impossible to plan the theoretical optimal route. To solve the above problem, this paper presents an optimized A* algorithm, the adaptive adjustment step algorithm and the three-time Bezier curve are used to solve the problems of many turning points, large turning angles, and long running time in the search path. Moreover, aiming at the path planning problem of mobile robots facing dynamic obstacle interference in complex environments, an algorithm that integrates the improved A* algorithm with the dynamic window method is proposed, which not only solves the shortcomings of the A* algorithm in which the dynamic obstacles cannot be avoided, but also prevents the mobile robot from falling into local optimization. The results show that the fusion algorithm of the improved A* algorithm and the dynamic window method with the traditional A* algorithm reduces the number of turns by 50% and the path length by 3.62% compared with the original algorithm. In the same environment, compared with the traditional algorithm, the hybrid algorithm in this paper reduces the average time consumption by 10.27%, the number of path inflection points by 57.14%, and the accuracy is higher than 33.33%, which is more effective in complex dynamic environments.

An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.

Space robot motion path planning based on fuzzy control algorithm

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.

Mobile Robot Path Planning Algorithm Based on Improved A Star

With the rapid development of artificial intelligence, path planning has become one of the key technologies in the application of mobile robots. Although the A star(A*)path planning algorithm can plan a collision-free path, there are still shortcomings such as the path is not optimal, too many redundant points, and not smooth enough. Aiming at the deficiencies of the A* algorithm, an improved A* algorithm is proposed. Firstly improved A* algorithm divides the distance between adjacent nodes in the path planned by A* algorithm. Then we use the two-way optimization method to delete useless nodes, short the path length and reduce the number of inflection points. Finally, a cubic spline function is introduce d to smooth the path. The simulation results show that the improved A* algorithm is effectively optimized in terms of path length, number of inflection points and smoothness.