An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock

Weerakoon, Tharindu; Ishii, Kazuo; Nassiraei, Amir Ali Forough

doi:10.1515/jaiscr-2015-0028

Cited by 88 publications

(36 citation statements)

References 38 publications

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“…Research work on robotic pathfinding methods can be divided into traditional path planning methods and neural network-based approaches. The former mainly uses local path planning algorithms such as ant colony algorithm [3] and artificial potential field method [4] to ensure that the robot is constantly looking for the optimal path during the obstacle avoidance process, but the robot has a high probability of falling into the local optimum, thus ignoring the global optimum path. The improved hybrid path planning method [5] applies local path planning algorithms to global maps, and although it can achieve global optimization to some extent, robots will show poor adaptation in unfamiliar environments.…”

Section: Related Workmentioning

confidence: 99%

“…In response to these issues, relevant scholars at home and abroad have carried out a great deal of research and made considerable progress. In traditional methods, robots use local path planning methods such as ant colony algorithm [3] and artificial potential field [4] to avoid obstacles in real time based on the information acquired by their sensors, but these approaches tend to be trapped in local optima and then lead to path oscillations. Hybrid path planning methods [5] combined with global map information and local optimization strategies significantly reduce the probability of a robot falling into a local optimum and improves the efficiency of path planning, but it is difficult to fully perceive complex and variable environments and does not take full advantage of visual features as important pathfinding information.…”

Section: Introductionmentioning

confidence: 99%

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A Virtual End-to-End Learning System for Robot Navigation Based on Temporal Dependencies

Zhang

Lyu

et al. 2020

IEEE Access

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Steering a wheeled mobile robot through a variety of environments is a complex task. To achieve this, many researchers have tried to convert front-facing camera data stream to the corresponding steering angles based on convolutional neural network model (CNN). However, most of existing methods suffer from higher cost of data acquisition and longer training cycles. To address these issues, this paper proposes an innovative end-to-end deep neural network model that fully considers the temporal relationships in the data and incorporates long short-term memory (LSTM) based on the CNN model. In addition, to obtain enough data to train and test the model, we establish a simulation system capable of creating realistic environment with various weather and road conditions and avoiding static and dynamic obstacles for robots. First, we use the system to capture the raw image sequence in different environments as a training set, and then we test the trained model in the system to realize an autonomous mobile robot that can adapt to various environments. The experimental results demonstrate that the proposed model not only can extract effectively and fully the features of road vision information with the highest correlation for navigation, but also can learn the time dependence of motion states and image features contained in a sequence.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Virtual End-to-End Learning System for Robot Navigation Based on Temporal Dependencies

Zhang

Lyu

et al. 2020

IEEE Access

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“…In order to overcome the above difficulties, some researchers have made different improvements to the traditional APF method. Weerakoon et al [4] solves the deadlock problem by replacing the traditional function with an exponential function. The APF method is also combined with other intelligent algorithms to improve the parameters of the intelligent algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel GRU-RNN Network Model for Dynamic Path Planning of Mobile Robot

et al. 2019

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A dynamic path planning method based on a gated recurrent unit-recurrent neural network model is proposed for the problem of path planning of a mobile robot in an unknown space. A deep neural network with sensor input is used to generate a new control strategy output to the physical model to control the movement of the robot and thus achieve collision avoidance behavior. Inputs and tags are derived from sample sets generated by an improved artificial potential field and an improved ant colony optimization algorithm. In order to make the ant colony algorithm converge quickly, the pheromone trail and the state transition probability are improved. The field function of the artificial potential field method is modified. Using the end-to-end network model to learn the mapping between input and output in the sample data, the direction and speed of the mobile robot are obtained. The simulation experiments and realistic simulations show that the network model can plan a reasonable path in an unknown environment. Compared with other traditional path planning algorithms, the proposed method is more robust than the traditional path planning algorithms to differences in the robot structure. INDEX TERMS Mobile robot, gated recurrent unit-recurrent neural network, dynamic path planning, ant colony optimization, artificial potential field.

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“…A new direction is synthesized using the robot motion information and direction between the robot and the goal point, which can guide the robot to escape the local minimum region. Weerakoon et al [18] propose an improved repulsive force for overcoming a local minima problem, which generates a new repulsive force to the primary force when the robot detects an obstacle within its sensory range. The new repulsive force component turns the robot smoothly away from the obstacles.…”

Section: Introductionmentioning

confidence: 99%

Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment

et al. 2019

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The artificial potential field approach provides a simple and effective motion planner for robot navigation. However, the traditional artificial potential field approach in practice can have a local minimum problem, i.e., the attractive force from the target position is in the balance with the repulsive force from the obstacle, such that the robot cannot escape from this situation and reach the target. Moreover, the moving object detection and avoidance is still a challenging problem with the current artificial potential field method. In this paper, we present an improved version of the artificial potential field method, which uses a dynamic window approach to solve the local minimum problem and define a danger index in the speed field for moving object avoidance. The new danger index considers not only the relative distance between the robot and the obstacle, but also the relative velocity according to the motion of the moving objects. In this way, the robot can find an optimized path to avoid local minimum and moving obstacles, which is proved by our experimental results.

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An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock

Cited by 88 publications

References 38 publications

A Virtual End-to-End Learning System for Robot Navigation Based on Temporal Dependencies

A Virtual End-to-End Learning System for Robot Navigation Based on Temporal Dependencies

A Novel GRU-RNN Network Model for Dynamic Path Planning of Mobile Robot

Smart Obstacle Avoidance Using a Danger Index for a Dynamic Environment

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