Path Planning in Uncertain Environment With Moving Obstacles Using Warm Start Cross Entropy

Tao, Xiuye; Lang, Ning; Li, Huiping; Xu, Dehong

doi:10.1109/tmech.2021.3071723

Cited by 13 publications

(6 citation statements)

References 45 publications

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“…The artificial potential field (PF) method [34,35] is another practical path planning method, which features simple structure and risk-aware computation in low-level control system [38]. In Tao et al [39], the framework of partially observable Markov decision process (POMDP) is utilized to deal with the planning problem, in which the environment is characterized as a grid map. The work by Li et al [40] proposed to eliminate the limitation of node movement direction in traditional A-star algorithm by extending its search neighborhood and design a process to remove the redundant subnodes in the same direction due to neighborhood extension.…”

Section: Introductionmentioning

confidence: 99%

Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

Zhao

Zhang

Liu

et al. 2023

Asian Journal of Control

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In the real‐world environment, the path planning method of tracked robot is widely studied when driving on uneven terrain. How to solve the problem that the traditional path planning algorithm cannot adapt to uneven terrain because of the constraints of obstacle avoidance and path length is a challenge for tracked robots. In this paper, a stability‐based path planning framework for tracked robot is proposed to reduce the risk of rollover when the tracked robot passes through uneven terrain. First, a virtual plane method is proposed to estimate the attitude of tracked robot. Second, on this basis, a dynamic high‐stability path evaluation algorithm for tracked robot based on force angle stability margin (FASM) is proposed, which transforms the stability‐based path planning problem into a hypergraph problem. Moreover, considering that the optimization problem is strongly nonlinear and nonconvex, a hybrid algorithm of covariance matrix adaptive evolution strategy (CMAES) and Levenberg–Marquardt (LM) is designed under the framework of generalized graph optimization (G2O) to improve the solution efficiency. Finally, simulation and experiments show that the stability‐based path planning framework can effectively plan the high‐quality path, and the maximum stability of the tracked robot is 0.9156 when the robot crosses uneven terrain using optimal path 2.

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Section: Introductionmentioning

confidence: 99%

Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

Zhao

Zhang

Liu

et al. 2023

Asian Journal of Control

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“…There are a number of path planning methods developed for robot navigation [4][5][6][7]. Among them, artificial potential field (APF) [8], cell decomposition [9], mathematical programming [10] and roadmap [11] are identified as fundamental path planning algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…The APF method is popular among researchers due to its many advantages, such as simplicity and adaptability. However, there are well-documented inherited problems in APF-based methods [7,12,13]. These inherited drawbacks, such as (i) trap situations or dead locks (local minima), (ii) no passage between closely spaced obstacles, (iii) oscillations in narrow corridors and (iv) the goal non-reachable-with-obstacles-nearby problem (GN-RON), have motivated researchers to improve the APF-based methods and overcome these problems [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Agoraphilic Navigation Algorithm for Use in Dynamic Environments with a Moving Goal

2023

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This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.

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“…To tackle this problem, the rapidly exploring random tree (RRT) and many variants extended from RRT (denoted by RRT+ in this paper) have been proposed in the robotics community. Numerous RRT+ algorithms have been verified to be effective in the applications of manipulators [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

FBi-RRT: a path planning algorithm for manipulators with heuristic node expansion

Xiao,

Zhang,

et al. 2023

Robotica

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This paper is concerned with the problem of collision-free path planning for manipulators in multi-obstacle scenarios. Aiming at overcoming the deficiencies of existing algorithms in excessive time consumption and poor expansion quality, a path planning algorithm named Fast Bi-directional Rapidly-exploring Random Tree (FBi-RRT) with novel heuristic node expansion is proposed, which includes a selective-expansion strategy and a vertical-exploration strategy. The selective-expansion strategy is designed to guide the selection of the nearest-neighbor node to avoid the repeated expansion failure, thereby shortening the overall planning time. Also, the vertical-exploration strategy is developed to regulate the expansion direction of the collision nodes to escape from the obstacle space with less blindness, thus improving the expansion quality and further reducing time cost. Compared with previous planning algorithms, FBi-RRT can generate a feasible path for manipulators in a drastically shorter time. To validate the effectiveness of the proposed heuristic node expansion, FBi-RRT is conducted on a 6-DOF manipulator and tested in five scenarios. The experimental results demonstrate that FBi-RRT outperforms the existing methods in time consumption and expansion quality.

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Path Planning in Uncertain Environment With Moving Obstacles Using Warm Start Cross Entropy

Cited by 13 publications

References 45 publications

Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

Path planning for a tracked robot traversing uneven terrains based on tip‐over stability

Machine Learning-Based Agoraphilic Navigation Algorithm for Use in Dynamic Environments with a Moving Goal

FBi-RRT: a path planning algorithm for manipulators with heuristic node expansion

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