Efficient path planning for mobile robots with adjustable wheel positions

In this paper, we propose a new path planning algorithm based on the probabilistic roadmaps method (PRM), in order to effectively solve the autonomous path planning of mobile robots in complex environments with multiple narrow channels. The improved PRM algorithm mainly improves the density and distribution of sampling points in the narrow channel, through a combination of the learning process of the PRM algorithm and the APF algorithm. We also shortened the required time and path length by optimizing the query process. The first key technology to improve the PRM algorithm involves optimizing the number and distribution of free points and collision-free lines in the free workspace. To ensure full visibility of the narrow channel, we extend the obstacles through the diagonal distance of the mobile robot while ignoring the safety distance. Considering the safety distance during movement, we re-classify the all sampling points obtained by the quasi-random sampling principle into three categories: free points, obstacle points, and adjacent points. Next, we transform obstacle points into the free points of the narrow channel by combining the APF algorithm and the characteristics of the narrow channel, increasing the density of sampling points in the narrow space. Then, we include potential energy judgment into the construction process of collision-free lines shortening the required time and reduce collisions with obstacles. Optimizing the query process of the PRM algorithm is the second key technology. To reduce the required time in the query process, we adapt the bidirectional A* algorithm to query these local paths and obtain an effective path to the target point. We also combine the path pruning technology with the potential energy function to obtain a short path without collisions. Finally, the experimental results demonstrate that the new PRM path planning technology can improve the density of free points in narrow spaces and achieve an optimized, collision-free path in complex environments with multiple narrow channels.

With the continuous progress and development of robotics, mobile robots have been widely used in many different fields. In the power grid, mobile robots are used to inspect electrical equipment, which greatly reduce the investment of manpower and material resources. However, in many cases, mobile robots need to work in a constantly changing and complex environment. Because they cannot obtain environmental information in time, it is often difficult to make path planning. In response to this problem, this paper proposes a path planning method for mobile robots based on improved reinforcement learning. This method establishes a grid environment model, defines the return value through the number of steps of the robot, and then proposes a changing action selection strategy for the balance between the robot’s exploration and utilization of the environment in reinforcement learning, so that the exploration factor dynamically change with the increase of the robot’s exploration degree of the environment, thus speeding up the convergence speed of the learning algorithm. Simulation results show that this method can realize autonomous navigation and path planning of mobile robots in complex environments. Compared with traditional algorithms, it greatly reduces the number of iterations.

This paper presents a path planning algorithm for autonomous navigation of nonholonomic mobile robots in complex environment. The irregular contour of obstacles is represented by segments. The goal of the robot is to move towards a known target while avoiding obstacles. The velocity constraints, kinematic robot model and nonholonomic constraint are considered in the problem. The optimal path planning problem is formulated as a constrained receding horizon planning problem and the trajectory is obtained by solving an optimal control problem with constraints. Local minima are avoided by choosing intermediate objectives based on the real time environment.

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a “high-intensity training” mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving a mobile robot path-planning problem.

Evolutionary algorithms (EA) have been used to solve path planning of mobile robots successfully. However, accuracy and convergence are always topics. The cloud model which transforms qualitative concept into quantitative description, and vice versa, is adept in uncertainty modeling. It can be used in EA for evolving in a uniform and natural way. An EA embedded with cloud model (EACM) for path planning of mobile robots was proposed in this paper. The algorithm adopted floating-point coding and realized fast converging. Simulation results verified the efficiency of our algorithm.

The path of the mobile robot is a multi-objective optimization problem. The traditional ant colony algorithm is introduced into the path planning of the mobile robot. Due to the complexity of the robot path, the large search space and the low efficiency, it is easy to fall into the local optimum and even deadlock and other defeats. In order to enhance the efficiency and accuracy of path planning, a path planning algorithm of mobile robot based on ant colony algorithm is proposed. The path planning of mobile robot is simulated under grid environment. The experimental results show that the improved algorithm can improve the efficiency of path planning for mobile robot, safely avoid obstacles and find all the optimal solutions in the shortest time.

In this paper, an elliptic influence domain and improved artificial potential field method (EID-IAPF) is proposed for online path planning of mobile robots. This paper has established a collision risk area model and has obtained dynamical elliptic influence domain of obstacles. Also, the velocity-repulsive potential equation is introduced to modify the traditional artificial potential field method (APF), which improves the capacity of path planning of the robots. Finally, a simulation of complex environment with narrow channels is constructed to verify the feasibility of the method, and to compare with the traditional APF. It proves that EID-IAPF can effectively improve the path planning efficiency of mobile robots.

Path planning is an important aspect in autonomous mobile robots. Various fuzzy logic methods are conveniently employed for path planning of mobile robots as they are most helpful in handling uncertain data. In this work the robot starts from a fixed initial location towards a goal destination without colliding with obstacles in between within a shortest path possible both in static as well as dynamic environment. Here an analysis of fuzzy rules for path planning of a mobile robot for various membership funcions is done. Both simulation results and real time implementations on I Robot create platform are shown in this paper. Fuzzy tool box in Matlab is used for simulation. Three combinations of membership functions with 24 rules are considered. These include-Trapezoidal and Triangular, Bell and Trapezoidal and finally triangular and Bell. The error between the calculated value and actual value indicate that the combination of Trapezoidal and Triangular is least.

Path planning is one of the most important and challenging problems of mobile robot. It is one of the keys that will make the mobile robots fully autonomous. In this paper, we summarized the application of soft computing approaches in path planning for mobile robot. Finally the future works of path planning for mobile robots are prospected.

Path planning is an important area of mobile robot research, and the ant colony optimization algorithm is essential for analyzing path planning. However, the current ant colony optimization algorithm applied to the path planning of mobile robots still has some limitations, including early blind search, slow convergence speed, and more turns. To overcome these problems, an improved ant colony optimization algorithm is proposed in this paper. In the improved algorithm, we introduce the idea of triangle inequality and a pseudo-random state transfer strategy to enhance the guidance of target points and improve the search efficiency and quality of the algorithm. In addition, we propose a pheromone update strategy based on the partition method with upper and lower limits on the pheromone concentration. This can not only improve the global search capability and convergence speed of the algorithm but also avoid the premature and stagnation phenomenon of the algorithm during the search. To prevent the ants from getting into a deadlock state, we introduce a backtracking mechanism to enable the ants to explore the solution space better. Finally, to verify the effectiveness of the proposed algorithm, the algorithm is compared with 11 existing methods for solving the robot path planning problem, including several ACO variants and two commonly used algorithms (A* algorithm and Dijkstra algorithm), and the experimental results show that the improved ACO algorithm can plan paths with faster convergence, shorter path lengths, and higher smoothness. Specifically, the algorithm produces the shortest path length with a standard deviation of zero while ensuring the most rapid convergence and the highest smoothness in the case of the shortest path in four different grid environments. These experimental results demonstrate the effectiveness of the proposed algorithm in path planning.

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.

Dynamic Window Approach (DWA) is widely used in the local path planning of mobile robot. DWA algorithm has the advantages of simple planning algorithm and good realtime performance. However, the DWA exists unreachable target point, unreasonable path planning in the dense obstacles and it cannot ensure both speed and security. Therefore, an online parameter self-adaptive DWA algorithm is proposed to automatically adjust the weight of the objective function on the basis of the size of alternative velocity space and the distance between the mobile robot to the target point. By adapting to the dynamic changes of the environment, the optimal running speed and reasonable path of mobile robot can be obtained. The simulation verified that the mobile robot can pass through the dense obstacles area and reach the target point safely and reliably. And the problems that the robot might move around the dense obstacles and result in an unsmooth path are avoided. and the operational efficiency of the mobile robot is ensured simultaneously.

Autonomous mobile robot encompasses modules such as perception, path planning, decision-making, and control. Among these modules, path planning serves as a prerequisite for mobile robots to accomplish tasks. Enhancing path planning capability of mobile robots can effectively save costs, reduce energy consumption, and improve work efficiency. The primary slime mold algorithm (SMA) exhibits characteristics such as a reduced number of parameters, strong robustness, and a relatively high level of exploratory ability. SMA performs well in path planning for mobile robots. However, it is prone to local optimization and lacks dynamic obstacle avoidance, making it less effective in real-world settings. This paper presents an enhanced SMA (ESMA) path-planning algorithm for mobile robots. The ESMA algorithm incorporates adaptive techniques to enhance global search capabilities and integrates an artificial potential field to improve dynamic obstacle avoidance. Compared to the SMA algorithm, the SMA-AGDE algorithm, which combines the Adaptive Guided Differential Evolution algorithm, and the Lévy Flight-Rotation SMA (LRSMA) algorithm, resulted in an average reduction in the minimum path length of (3.92%, 8.93%, 2.73%), along with corresponding reductions in path minimum values and processing times. Experiments show ESMA can find shortest collision-free paths for mobile robots in both static and dynamic environments.

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.

Path planning is essential for mobile robots to operate efficiently in uncertain environments. As the number of robots increases, centralised approaches struggle to provide feasible solutions within real-time constraints. To address this, an adaptive hybrid strategy-based decentralised path planning algorithm is proposed to solve the real-time path planning problem through decentralised computing. First, various path planning strategies are introduced, including a neural computing planning strategy, a dynamic search planning strategy, and a cluster coordination planning strategy. Then, an intelligent strategy selection mechanism with an adaptive strategy adjustment factor is designed, allowing each robot to dynamically select optimal planning strategies based on their current state and ensuring the planned paths exhibit greater flexibility and adaptability. Finally, the results of the ablation experiment indicate that all three strategies effectively enhance the navigation capabilities of robots in a decentralised mode. The algorithm comparison experiment demonstrates that the proposed algorithm achieves a higher task completion rate and a lower detour percentage in various environments. The decision response experiment shows that our approach has an average decision-making time of approximately 60 ms, which meets the real-time requirements for decentralised path planning of mobile robots in most scenarios.