Local Path Planning Method Based on Virtual Manipulators and Dynamic Window Approach for a Wheeled Mobile Robot

Foresighted navigation is an essential skill for robots to rise from rigid factory floor installations to much more versatile mobile robots that partake in our everyday environment. The current state of the art that provides this mobility to some extent is the Dynamic Window Approach combined with a global start-to-target path planner. However, neither the Dynamic Window Approach nor the path planner are equipped to predict the motion of other objects in the environment. We propose a change in the Dynamic Window Approach—a dynamic collision model—that is capable of predicting future collisions with the environment by also taking into account the motion of other objects. We show in simulated experiments that our new way of computing the Dynamic Window Approach significantly reduces the number of collisions in a dynamic setting with nonholonomic vehicles while still being computationally efficient.

Mobile robot path planning in an unknown environment is a fundamental and challenging problem in the field of robotics. Dynamic window approach (DWA) is an effective method of local path planning, however some of its evaluation functions are inadequate and the algorithm for choosing the weights of these functions is lacking, which makes it highly dependent on the global reference and prone to fail in an unknown environment. In this paper, an improved DWA based on Q-learning is proposed. First, the original evaluation functions are modified and extended by adding two new evaluation functions to enhance the performance of global navigation. Then, considering the balance of effectiveness and speed, we define the state space, action space and reward function of the adopted Q-learning algorithm for the robot motion planning. After that, the parameters of the proposed DWA are adaptively learned by Q-learning and a trained agent is obtained to adapt to the unknown environment. At last, by a series of comparative simulations, the proposed method shows higher navigation efficiency and successful rate in the complex unknown environment. The proposed method is also validated in experiments based on XQ-4 Pro robot to verify its navigation capability in both static and dynamic environment.

The traditional Rapidly-exploring Random Tree Star (RRT*) suffers from the low path generation efficiency, numerous invalid exploration points, and unsuitability for navigation in unknown dynamic environments. In this article, we propose a dynamic path planning scheme by combining the improved RRT* and the improved dynamic window approach (DWA). For pregenerating an initial path, we use the artificial potential field (APF) method to expand new nodes. The adaptive dynamic step-size is introduced for accelerating the optimal path searching. Moreover, the improved ant colony algorithm is used to perform multiobjective optimization on the generated initial path. When unknown obstacles appear in the path, the improved DWA is developed for obstacle avoidance. Finally, the proposed method is validated by simulation and experiment in both of the static and dynamic environments. In particular, the simulation results show that, compared with some existing methods, our algorithm can generate a higher-quality initial path in the static environment and avoid unknown dynamic obstacles effectively in the dynamic environment. Furthermore, we implement our algorithm in a mobile robot to verify the correctness and effectiveness in the practical scenario.

The study of path-planning algorithms is crucial for an electric logistics vehicle to reach its target point quickly and safely. In light of this, this work suggests a novel path-planning technique based on the improved A-star (A*) fusion dynamic window approach (DWA). First, compared to the A* algorithm, the upgraded A* algorithm not only avoids the obstruction border but also removes unnecessary nodes and minimizes turning angles. Then, the DWA algorithm is fused with the enhanced A* algorithm to achieve dynamic obstacle avoidance. In addition to RVIZ of ROS, MATLAB simulates and verifies the upgraded A* algorithm and the A* fused DWA. The MATLAB simulation results demonstrate that the approach based on the enhanced A* algorithm combined with DWA not only shortens the path by 4.56% when compared to the A* algorithm but also smooths the path and has dynamic obstacle-avoidance capabilities. The path length is cut by 8.99% and the search time is cut by 16.26% when compared to the DWA. The findings demonstrate that the enhanced method in this study successfully addresses the issues that the A* algorithm’s path is not smooth, dynamic obstacle avoidance cannot be performed, and DWA cannot be both globally optimal.

This article proposes, a novel obstacle avoidance algorithm for a mobile robot based on finite memory filtering (FMF) in unknown dynamic environments. To overcome the limitations of the existing dynamic window approach (DWA), we propose a new version of the DWA, called the finite distribution estimation-based dynamic window approach (FDEDWA), which is an algorithm that avoids dynamic obstacles through estimating the overall distribution of obstacles. FDEDWA estimates the distribution of obstacles through the FMF, and predicts the future distribution of obstacles. The FMF is derived to minimize the effect of the measurement noise through the Frobenius norm, and covariance matrix adaptation evolution strategy. The estimated information is used to derive the control input for the robust mobile robot navigation effectively. FDEDWA allows for the fast perception of the dynamic environment, and superior estimation performance, and the mobile robot can be controlled by a more optimal path while maintaining real-time performance. To demonstrate the performance of the proposed algorithm, simulations, and experiments were carried out under dynamic environments by comparing the latest dynamic window for dynamic obstacle, and the existing DWA.

This paper presents a real-time path planning approach for controlling the motion of space-based robots. The algorithm can plan three-dimensional trajectories for agents in a complex environment which includes numerous static and dynamic obstacles, path constraints, and/or performance constraints. This approach is extended based on the dynamic window approach (DWA). As the classic reactive method for obstacle avoidance, DWA uses an optimized function to select the best motion command. The original DWA optimization function consists of three weight terms. Changing the weights of these terms will change the behavior of the algorithm. In this paper, to improve the evaluation ability of the optimization function and the robot’s ability to adapt to the environment, a new optimization function is designed and combined with fuzzy logic to adjust the weights of each parameter of the optimization function. Given that DWA has the defect of local minima, which makes the robot hard to escape U-shaped obstacles, a dual dynamic window method and local goals are adopted in this article to help the robot escape local minima. By comparison, the proposed method is superior to traditional DWA and fuzzy DWA (F_DWA) in terms of computational efficiency, smoothness and security.

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.

Multi-robot path planning faces challenges such as conflict avoidance, collaboration, and dynamic environments. This paper proposes a multi-robot path planning algorithm that integrates the improved nutcracker optimization algorithm with the improved dynamic window approach. To address the nutcracker algorithm’s sensitivity to initial conditions and slow convergence, a population initialization strategy is introduced for more diverse initial populations. Additionally, a simplified path node strategy is also designed to shorten paths and reduce steering times. By incorporating a dynamic inertia weight factor , the balance between global exploration and local optimization is improved. To address the limitations of the dynamic window approach, which is unable to avoid dynamic obstacles instantly and is prone to falling into local optimal solutions, the target distance subfunction, the path evaluation subfunction and the deviation from danger zone subfunction are added to the evaluation function. Finally, the two algorithms were fused together and we conducted four experiments to validate the performance of the MANOA, IDWA, and MANOA-IDWA algorithms, as well as the application of MANOA-IDWA in multi-robot path planning. Results show that MANOA-IDWA significantly increases path planning success rates in dynamic environments, producing shorter and smoother paths, thus enhancing the safety and stability of multi-robot operations.

In order to solve the problems of large storage, excessive redundant nodes and inflection points of paths, and unsmooth track in the traditional A*algorithm. This paper presents a fusion algorithm of mobile robot path planning based on the improved A* and dynamic window approach. Firstly, selectively expand the neighborhood according to the connection between the node to be expanded and the target node, and reduce the expanded nodes in openlist and closelist. Secondly, according to the key node extraction strategy, eliminate the redundant path nodes and unnecessary inflection points generated by A* tracking. Finally, the dynamic window approach is integrated to carry out real-time dynamic path planning. The Matlab simulation experiment results show that the improved fusion algorithm shortens the path length, and the smoothness and security of the path are also optimized.

To address the critical limitations of conventional Grey Wolf Optimization (GWO) in path planning scenarios—including insufficient exploration capability during the initial phase, proneness to local optima entrapment, and inherent deficiency in dynamic obstacle avoidance—this paper proposes a multi-strategy enhanced GWO algorithm. Firstly, the Piecewise chaotic mapping is applied to initialize the Grey Wolf population, enhancing the initial population quality. Secondly, the linear convergence factor is modified to a nonlinear one to balance the algorithm’s global and local search capabilities. Thirdly, Evolutionary Population Dynamics (EPD) is incorporated to enhance the algorithm’s ability to escape local optima, and dynamic weights are used to improve convergence speed and accuracy. Finally, the algorithm is integrated with the Improved Dynamic Window Approach (IDWA) to enhance path smoothness and perform dynamic obstacle avoidance. The proposed algorithm is named PAGWO-IDWA. The results demonstrate that, compared to traditional GWO, PAGWO-IDWA reduces the path length, number of turns, and running time by 9.58%, 33.16%, and 30.31%, respectively. PAGWO-IDWA not only overcomes the limitations of traditional GWO but also enables effective path planning in dynamic environments, generating paths that are both safe and smooth, thus validating the effectiveness of the algorithm.

This paper studies dynamic obstacle avoidance strategies of quadruped robots in an unknown and dynamically changing environment. Different from classical navigation problems with static environment, an unknown number of moving obstacles with unknown dynamics are present in the scenario considered in this paper. This unknown and rapidly changing environment prevents us from directly applying existing navigation approaches for quadruped robots. To address the additional challenges introduced by the dynamic obstacles, an improved dynamic window approach (DWA) is proposed in which both evaluation function and constraints are modified. To enable real-time implementation of the proposed algorithm, multi-layer techniques for processing camera point cloud data are applied for online extraction of environmental information. The proposed algorithm is tested both in simulation and on hardware with a quadruped robot equipped with a low-cost RGB-D camera. The results show that the proposed algorithm significantly increases the rate of success for avoiding dynamical obstacles and reduces the time spent for reaching the target.

An improved dynamic window method is designed as a local path planning method. Aiming at the problem of unreasonable path selection in traditional dynamic window approach when it is close to the target point and dense obstacles, an improved dynamic window approach is proposed. A new objective function is introduced in the algorithm to improve the speed selection when the robot approaches the target point. The number of obstacles is introduced into the velocity evaluation function to improve the problem that dynamic window approach is easy to bypass dense obstacles. Simulation experiments in different environments show the effectiveness of the improved algorithm.

Intelligent mobile robots play an important role in the green and efficient operation of warehouses and have a significant impact on the natural environment and the economy. Path planning technology is one of the key technologies to achieve intelligent mobile robots. In order to improve the pickup efficiency and to reduce the resource waste and carbon emissions in logistics, we investigate the robot path optimization problem. Under the guidance of the sustainable development theory, we aim to achieve the goal of environmental social governance by shortening and smoothing robot paths. To improve the robot’s ability to avoid dynamic obstacles and to quickly solve shorter and smoother robot paths, we propose a fusion algorithm based on the improved genetic algorithm and the dynamic window approach. By doing so, we can improve the efficiency of warehouse operations and reduce logistics costs, whilst also contributing to the realization of a green supply chain. In this paper, we implement an improved fusion algorithm for mobile robot path planning and illustrate the superiority of our algorithm through comparative experiments. The authors’ findings and conclusions emphasize the importance of using advanced algorithms to optimize robot paths and suggest potential avenues for future research.

PurposeThe purpose of this paper is to solve the problem that mobile robots are still based on reactive collision avoidance in unknown dynamic environments leading to a lack of interaction with obstacles and limiting the comprehensive performance of mobile robots. A dynamic window approach with multiple interaction strategies (DWA-MIS) is proposed to solve this problem.Design/methodology/approachThe algorithm firstly classifies the moving obstacle movement intention, based on which a rule function is designed to incorporate positive incentives to motivate the robot to make correct avoidance actions. Then, the evaluation mechanism is improved by considering the time cost and future information of the environment to increase the motion states. Finally, the optimal objective function is designed based on genetic algorithm to adapt to different environments with time-varying multiparameter optimization.FindingsFaced with obstacles in different states, the mobile robot can choose a suitable interaction strategy, which solves the limitations of the original DWA evaluation function and avoids the defects of reactive collision avoidance. Simulation results show that the algorithm can efficiently adapt to unknown dynamic environments, has less path length and iterations and has a high comprehensive performance.Originality/valueA DWA-MIS is proposed, which increases the interaction capability between mobile robots and obstacles by improving the evaluation function mechanism and broadens the navigation strategy of DWA at a lower computational cost. After real machine verification, the algorithm has a high comprehensive performance based on real environment and provides a new idea for local path planning methods.

This paper describes a sampling strategy for the dynamic window approach (DWA), a local path planner, for omni-directional robot motion planning. An efficient local planner allows the robot to quickly respond to dynamic obstacles and ensures that commanded velocities meet the dynamic constraints of the robot. While typical DWA implementations sample the velocity space evenly, we propose that targeted sampling (TS) will result in a more fine-grained search of the relevant velocity space, leading to better control and performance in space-constrained environments. Our TS-DWA strategy is informed by the global planned path, allowing us to sample more velocities in the general path direction. We employ a polar velocity generator to selectively sample velocities and couple angular velocity samples to the path curvature. A bias for angular velocity is added for robots with a preferred heading, such as robots with forward-mounted sensors, to quickly turn toward the desired direction for better sensing. The strategy is implemented as a robot operating system (ROS) navigation stack local_planner plugin and tested in simulation with Gazebo using an omni-directional robot platform. Experiments show that as the space around the simulated robot gets smaller, our proposed sampling strategy results in more successful navigation trials to the goal in space-constrained environments compared to other commonly used methods like DWA and timed-elastic-band, where planning fails or oscillates. TS-DWA was also deployed on the Vision60 quadrupedal robot to demonstrate navigation through narrow corridors in the real world.