Improved hybrid A* path planning method for spherical mobile robot based on pendulum

Zhang, Ziang; Wan, Yixu; You, Wang; Guan, Xinping; Ren, Wei; Li, Guang

doi:10.1177/1729881421992958

Cited by 29 publications

(9 citation statements)

References 25 publications

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“…Hybrid A* algorithm adds non‐holonomic constraints based on a wheeled robot [19], but since spherical robot's kinematic model differs from that of wheeled robot, spherical robot frequently finds it challenging to follow the planned path. By fully considering the law of lateral motion and tilt of spherical robots, we designed an online global path planning algorithm suitable for spherical robots by improving Hybrid A* algorithm's node expansion rule in our previous work [20], and in this paper we further improve it.…”

Section: Planningmentioning

confidence: 99%

Spherical robot: A novel robot for exploration in harsh unknown environments

Ren,

Wang,

Liu

et al. 2023

IET Cyber-Syst and Robotics

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The authors propose a complete software and hardware framework for a novel spherical robot to cope with exploration in harsh and unknown environments. The proposed robot is driven by a heavy pendulum covered by a fully enclosed spherical shell, which is strongly protected, amphibious, anti‐overturn and has a long‐battery‐life. Algorithms for location and perception, planning and motion control are comprehensively designed. On the one hand, the authors fully consider the kinematic model of a spherical robot, propose a positioning algorithm that fuses data from inertial measurement units, motor encoder and Global Navigation Satellite System, improve global path planning algorithm based on Hybrid A* and design an instruction planning controller based on model predictive control (MPC). On the other hand, the dynamic model is built, linear MPC and robust servo linear quadratic regulator algorithm is improved, and a speed controller and a direction controller are designed. In addition, based on the pose and motion characteristics of a spherical robot, a visual obstacle perception algorithm and an electronic image stabilisation algorithm are designed. Finally, the authors build physical systems to verify the effectiveness of the above algorithms through experiments.

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Section: Planningmentioning

confidence: 99%

Spherical robot: A novel robot for exploration in harsh unknown environments

Ren,

Wang,

Liu

et al. 2023

IET Cyber-Syst and Robotics

Self Cite

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“…The simulation results show that, compared with RRT and RRT connection algorithm, the planning ability and path optimization ability of this algorithm are improved [15]. Zhang Z et al proposed a path planning method based on the improved A* algorithm according to the special kinematic characteristics of the spherical motion robot, which improved the search efficiency of the robot and could find the optimal path in a short time [16]. Zhang TW et al proposed an improved firefly algorithm (FA) combined with genetic algorithm (GA) to solve the defect of local optimal solution of FA algorithm.…”

Section: Related Workmentioning

confidence: 99%

Research on Key Technologies of Smart City Building Interior Decoration Construction based on In-Depth Learning

Zhang¹,

Qin²

2022

IJACSA

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The intelligentization of building interior decoration construction is of great significance to the construction of smart city, and robot automation has brought an opportunity for this. Robot self-decoration is the development trend in the future. One of the key issues involved, is the self-planning of mobile path. In this regard, the research adopts the proximal policy optimization algorithms (PPO) to improve the self-planning path ability of the decoration robot. For the information of lidar and robot status, the Full Connect Neural Network (FCNN) is used to process it. In addition, the reward function and the corresponding Credit Assignment Problem (CAP) model are designed, to accelerate the learning process of path planning. Aiming at the dynamic uncertainty in the actual environment, the adaptive loss function is used to build an auxiliary model to predict the environmental change. The simulation results show that the research and design strategy significantly improves the learning efficiency and path planning success rate of the decoration robot, and shows good adaptability to the dynamic environment, which has important reference significance for the practical application of the decoration robot.

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“…Path planning refers to finding -through algorithms and data structures -a feasible path from the starting point to the target point under the autonomous control of a mobile robot and avoiding collisions with obstacles in the environment [1]. Standard path planning algorithms include A* algorithm [2], RRT algorithm [3], Ant Colony Optimization (ACO) [4], genetic algorithm [5], particle swarm algorithm [6], and so forth. Among them, ACO is widely used in path planning because of its strong global search capability and ease of combining with other algorithms.…”

Section: Introductionmentioning

confidence: 99%

A path planning method for mobile robots incorporating artificial potential field method and ant colony algorithm

Xu,

Jin,

Zhang

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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For the problems of long planning paths and slow convergence speed of the ant colony optimization (ACO) in mobile robot path planning, this paper improves the ant colony optimization and introduces the artificial potential field method (APF). Firstly, the initial pheromone concentration is differentiated to make the ants more directional at the early stage of the algorithm. Second, the heuristic factor in the heuristic function is dynamically adjusted to optimize the pheromone update strategy so that the algorithm can effectively distinguish the high-quality paths from the common ones. Finally, the distance parameter is added to the repulsion function of APF, the improved ant colony algorithm is fused with APF, and a fusion algorithm based on the improved ant colony algorithm-artificial potential field method (IACO-APF) is proposed. In the simulation experiments, the trajectories are optimized by the B-spline curve method, and the experiments show that the IACO-APF algorithm can obtain better solutions in a shorter time compared with the traditional ant colony algorithm and other algorithms mentioned in the literature.

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Improved hybrid A* path planning method for spherical mobile robot based on pendulum

Cited by 29 publications

References 25 publications

Spherical robot: A novel robot for exploration in harsh unknown environments

Spherical robot: A novel robot for exploration in harsh unknown environments

Research on Key Technologies of Smart City Building Interior Decoration Construction based on In-Depth Learning

A path planning method for mobile robots incorporating artificial potential field method and ant colony algorithm

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