Three-Dimensional Path Planning of a Climbing Robot Using Mixed Integer Linear Programming

Ronggang, Yue; Xiao, Jizhong; Wang, Shaoping; Joseph, Samleo L.

doi:10.1163/016918610x534277

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Despite the simplicity and easy handling, the shortest-path solution could hardly be used under a multi-constraint environment, thereby showing great limitations. Some scholars started from the perspective of energy for motion path planning of wall surface inspection robots in complex environments, selecting the optimal path via comprehensive evaluation of the consumed energy [33][34][35][36]. Meanwhile, some others introduced the penalty function for the selection of the optimal path [37][38][39].…”

Section: Related Workmentioning

confidence: 99%

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

Tao

Wen

et al. 2022

Electronics

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A wall surface inspection robot mainly relies on the inertial measurement unit and global positioning system (GPS) signal during intelligent wall surface inspection. The robot may encounter incorrect positioning under a GPS-denied environment, easily triggering safety accidents. In order to obtain a path suitable for the safe work of the robot wall surface inspection robot in a GPS-denied environment, a global path-planning method for wall surface inspection robots was proposed based on the improved generic algorithm (GA). The influencing factor for GPS signal strength was introduced into the heuristic function in path planning for GA to address the aforementioned problem. Using the PSO algorithm, GA was initialized and the influencing term of GPS signal was introduced into the fitness degree function so as to achieve point-to-point path planning of vertical wall surface inspection robot. Path angle and probability of intersection and variation was taken into account for better path planning capability. Finally, the simulation experiments were performed. The generated path using the improved GA was found to avoid the blind area of the GPS signal. The algorithm proposed has a good performance with average convergence times of 35.9 times and an angle of 55.88° in simple environment. Contrary to the traditional GA and PSO algorithm, the method showed advantages in terms of the convergence rate, path quality, path angle change, and algorithm stability. The research presented in this article is meaningful and relatively sufficient. The simulation test is also quite convincing. The proposed method was proved to be effective in global path planning for a wall surface inspection robot.

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Section: Related Workmentioning

confidence: 99%

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

Tao

Wen

et al. 2022

Electronics

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“…We first calculate the standard normal distribution of the interval [−2, 2] and then take 41 intervals of 0.1 normal distribution values. Each arc is determined according to the content of Definition 3 (see (17)). Consider…”

Section: Environment Modelingmentioning

confidence: 99%

“…These methods usually use external measurement to obtain the path planning of terrain two-dimensional coordinates and three-dimensional altitude information to establish digital terrain [10][11][12][13] and estimate its trafficability [14]. These traditional approaches solve the path planning problem to achieve two goals: shortest path [15][16][17] and obstacle avoidance demand [18]. However, in the face of an unstructured dynamic path environment formed by geological structure and stochastic dynamic generation, traditional space environment modeling should also consider topography and geology, pose adjustment cost, and walking constraints in addition to the realization of the shortest path planning target and obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

Stochastic C-GNet Environment Modeling and Path Planning Optimization in a Narrow and Long Space

Yang

Tang

Wang

et al. 2018

Mathematical Problems in Engineering

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This study proposes a novel method of optimal path planning in stochastic constraint network scenarios. We present a dynamic stochastic grid network model containing semienclosed narrow and long constraint information according to the unstructured environment of an underground or mine tunnel. This novel environment modeling (stochastic constraint grid network) computes the most likely global path in terms of a defined minimum traffic cost for a roadheader in such unstructured environments. Designing high-dimensional constraint vector and traffic cost in nodes and arcs based on two- and three-dimensional terrain elevation data in a grid network, this study considers the walking and space constraints of a roadheader to construct the network topology for the traffic cost value weights. The improved algorithm of variation self-adapting particle swarm optimization is proposed to optimize the regional path. The experimental results both in the simulation and in the actual test model settings illustrate the performance of the described approach, where a hybrid, centralized-distributed modeling method with path planning capabilities is used.

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“…;(ii) methods based on virtual potential fields and navigation functions, represented by artificial potential methods [8,9], etc. ;(iii) methods based on mathematic optimization, represented by rolling optimization [10], linear programming [11], etc. ; and (iv) methods based on biological intelligence, represented by ant colony algorithms [12], particle swarm algorithms [13], genetic algorithms [14,15], neural networks [16], etc.…”

Section: Introductionmentioning

confidence: 99%

Collision-free trajectory planning for multi-robot simultaneous motion in preforms weaving

Meng

et al. 2022

Robotica

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In this paper, an automatic obstacle avoidance trajectory planning strategy is proposed for the simultaneous motion of multi-robots, which perform anthropomorphic skill operations in a large curved preformed three-dimensional (3D) weaving environment with multiple obstacles and limited space, to eliminate tedious manual calibration work of robot path in engineering. Firstly, an Adaptive Goal-guided Rapidly-exploring Random Trees (AGG-RRT) algorithm is proposed, combined with the robot obstacle avoidance strategy, to search the discrete position of the collision-free path of the end-effector gradually from the starting point to the ending point. Then the optimization of the path is completed by bidirectional pruning of redundant nodes and cubic non-uniform rational B-spline (NURBS) curve fitting. And finally, the robot trajectory is interpolated based on S-shaped acceleration/deceleration planning to ensure smooth robot joint motion. The simulation results demonstrate the superiority of the AGG-RRT algorithm over the basic RRT algorithm and related improved algorithms in terms of search time and success rate. The simulated experiments also achieve the smooth trajectory planning of multiple robotic arms with the synchronous obstacle avoidance motion, which shows that the AGG-RRT algorithm is successfully applied and the collision-free trajectory planning strategy is effective.

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Three-Dimensional Path Planning of a Climbing Robot Using Mixed Integer Linear Programming

Cited by 10 publications

References 7 publications

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

Stochastic C-GNet Environment Modeling and Path Planning Optimization in a Narrow and Long Space

Collision-free trajectory planning for multi-robot simultaneous motion in preforms weaving

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