A Global-Local Coupling Two-Stage Path Planning Method for Mobile Robots

Jian, Zhiqiang; Zhang, Songyi; Chen, Shitao; Nan, Zhixiong; Zheng, Nanning

doi:10.1109/lra.2021.3074878

Cited by 36 publications

(12 citation statements)

References 17 publications

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“…Local path planning requires mobile robots to obtain information from the environment constantly through sensors and real-time path planning. erefore, local path planning belongs to dynamic planning and is also called online planning [9][10][11]. At present, research on navigation strategies using deep reinforcement learning has attracted considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots

Zhang

Guo-yong

2022

Journal of Robotics

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A reinforcement learning-based continuous action space path planning method for mobile robots is proposed in this article. First, the kinematic model of the mobile robot is analyzed, and on this basis, the optimal state space is constructed according to the minimum depth of the field value in the depth image to characterize the distance between the robot and the obstacle. Then, by setting the reward function of the mobile robot based on the artificial potential field method, the information of the robot’s distance from obstacles is continuous, and a new reinforcement learning training process is proposed. Finally, by introducing a DDPG algorithm, the path planning of a mobile robot in an unknown environment is described as a Markov decision process, and the optimal planning of the mobile robot’s continuous action space path is realized with a high success rate. The results show that compared with other three comparison methods, the final success rates of the proposed method are the highest, which are 97.2%, 99.1%, 98.4%, and 98.6%, respectively.

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Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots

Zhang

Guo-yong

2022

Journal of Robotics

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“…In conclusion, by employing the combined (4), ( 5), ( 6), (9), and (10), social space for static groups can be established. Notably, when…”

Section: Static Groups Social Spacementioning

confidence: 98%

Human-Robot Harmonious Coexistence: A Robot Navigation Framework Follows Social Norms in Dynamic Scenarios

LingliYu,

JiaweiLuo,

WeiweiZhu

et al. 2023

Preprint

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Designing a path planner that provides humans with a comfortable experience is a crucial step in enabling robots to seamlessly integrate into human environments. In this paper, we propose a comprehensive framework that equips robots with the ability to navigate in human environments by taking into account social norms and predicting human trajectories. Firstly, we propose a generalized social space modeling method based on Gaussian Mixture Model (GMM). This model is used to constrain the robot's adherence to social norms and incorporates factors such as human posture, velocity, and group distribution. Secondly, to achieve collision-free navigation, we introduce a trajectory prediction method using a four-parameter logistic curve. This method considers human historical trajectory information, velocity constraints, and incorporates confidence weights. Finally, we use piecewise high-order polynomials to optimize robot's local trajectory spatiotemporally. The collision avoidance constraints between the robot and humans are carefully designed to maximize human comfort and robot sociability. To validate the effectiveness of our approach, we compare it with existing methods, and the results demonstrate a significant improvement in human comfort.

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“…The path planning algorithm of mobile robots has been deeply studied at home and abroad, and the results are remarkable. Traditional path planning algorithms mainly include artificial potential field method [10], element decomposition method [11], graph search algorithm [12], etc., but when the obstacles are complex, there are many disadvantages, such as large amount of calculation, easy to fall into local optimum, and the obtained path is not smooth, easy to appear sharp points, which is not in line with the actual situation, increasing the workload of mobile robots [13,14]. At present, many experts use heuristic algorithms to optimize path planning [15], including genetic algorithm (GA) [16], particle swarm optimization (PSO) [17], artificial bee colony algorithm (ABC) [18], grey wolf algorithm (GWO) [19], ant colony algorithm (ACO) [20], differential evolution algorithm (DE) [21], etc., and obtain good results.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the fitness value Fit, calculate the individual best position Pi t , the local best position Lbesti t , and elect Adm according to (12) to obtain the global best position.…”

Section: Step4mentioning

confidence: 99%

Path Planning of Mobile Robots Based on An Improved Particle Swarm Optimization Algorithm

Yuan

Sun

2022

Preprint

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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 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 mobile robot path planning problem.

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A Global-Local Coupling Two-Stage Path Planning Method for Mobile Robots

Cited by 36 publications

References 17 publications

Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots

Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots

Human-Robot Harmonious Coexistence: A Robot Navigation Framework Follows Social Norms in Dynamic Scenarios

Path Planning of Mobile Robots Based on An Improved Particle Swarm Optimization Algorithm

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