Adaptive Dynamic Window Approach for Local Navigation

Dobrevski, Matej; Skočaj, Danijel

doi:10.1109/iros45743.2020.9340927

Cited by 26 publications

(13 citation statements)

References 22 publications

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“…c obs is the distance from the robot and the obstacle. The optimal path is chosen from path candidates by DWA to maximize the cost function (21). Finally, velocities of the optimal path define optimal translational and angular velocities v opt and ω opt .…”

Section: Optimal Path (Step 3)mentioning

confidence: 99%

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Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Kobayashi

Motoi

2022

IEEE Access

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Local path planning considering static and dynamic obstacles for a mobile robot is one of challenging research topics. Conventional local path planning methods generate path candidates by assuming constant velocities for a certain period time. Therefore, path candidates consist of straight line and arc paths. These path candidates are not suitable for dynamic environments and narrow spaces. This paper proposes a novel local path planning method based on dynamic window approach with virtual manipulators (DWV). DWV consists of dynamic window approach (DWA) and virtual manipulator (VM). DWA is the local path planning method that performs obstacle avoidance for static obstacles under robot constraints. DWA also generates straight line and arc path candidates by assuming constant velocities. VM generates velocities of reflective motion by using virtual manipulators and environmental information. DWV generates path candidates by variable velocities modified by VM and predicted positions of static and dynamic obstacles. Therefore, in an environment with dynamic obstacles, the obstacle-avoidable paths which include non-straight line and non-arc paths are generated. The effectiveness of the proposed method was confirmed from simulation and experimental results.

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Section: Optimal Path (Step 3)mentioning

confidence: 99%

“…DWA selects the optimal path from path candidates. Dobrevski et al reported local path planning based on DWA and deep reinforcement learning to improve path optimization [21]. Liu et al developed the global dynamic path planning fusion algorithm combining jump-A* algorithm and DWA [22].…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Kobayashi

Motoi

2022

IEEE Access

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“…By mixing Dijkstra's algorithm and the DWA, it is possible to attain the desired position with the information provided by a SLAM system, as Liu et al [23] summarised. Another fusion option is discussed by Dobrevski et al [24] in their work, where they manage a convolutional neural network to select the parameters of the DWA algorithm. This provides a combination between data-driven learning and the dynamic model of the mobile robot.…”

Section: Path Followingmentioning

confidence: 99%

Dynamical Analysis of a Navigation Algorithm

et al. 2021

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There is presently a need for more robust navigation algorithms for autonomous industrial vehicles. These have reasonably guaranteed the adequate reliability of the navigation. In the current work, the stability of a modified algorithm for collision-free guiding of this type of vehicle is ensured. A lateral control and a longitudinal control are implemented. To demonstrate their viability, a stability analysis employing the Lyapunov method is carried out. In addition, this mathematical analysis enables the constants of the designed algorithm to be determined. In conjunction with the navigation algorithm, the present work satisfactorily solves the localization problem, also known as simultaneous localization and mapping (SLAM). Simultaneously, a convolutional neural network is managed, which is used to calculate the trajectory to be followed by the AGV, by implementing the artificial vision. The use of neural networks for image processing is considered to constitute the most robust and flexible method for realising a navigation algorithm. In this way, the autonomous vehicle is provided with considerable autonomy. It can be regarded that the designed algorithm is adequate, being able to trace any type of path.

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“…In the second aspect, Dobrevski and Skoˇcaj (2021) proposed an improved method for mobile robot navigation in unknown environments using an improved DWA combined with a deep q -network. Abubakr et al (2022) used fuzzy control in the DWA algorithm to fine-tune the weights of the trajectory evaluation function.…”

Section: Introductionmentioning

confidence: 99%

Multiple interaction strategies for mobile robots based on improved dynamic window approach in unknown dynamic environments

He,

Zhang,

Zhang

et al. 2023

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Purpose The 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/approach The 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. Findings Faced 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/value A 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.

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Adaptive Dynamic Window Approach for Local Navigation

Cited by 26 publications

References 22 publications

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Dynamical Analysis of a Navigation Algorithm

Multiple interaction strategies for mobile robots based on improved dynamic window approach in unknown dynamic environments

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