Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method

Fang, Zheng; Liang, Xifeng

doi:10.1108/ir-09-2021-0194

Cited by 16 publications

(4 citation statements)

References 29 publications

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“…Other approaches able to generate complex and collision-free movements are grounded on (i) grid-based [8] or interval-based [9] search methods, which find optimal obstaclefree paths for both the manipulator and mobile base; (ii) reward-based algorithms, which require the robot to try different paths, whereby it will be rewarded positively or negatively if it is successful or not [10,11]; (iii) artificial potential-fields algorithms, which generate attractive or repulsive paths for the manipulator joints and mobile base [12,13]; and (iv) sampling-based algorithms, which find an optimal path from roadmaps [14] or probabilistic methods [15,16].…”

Section: Introductionmentioning

confidence: 99%

Robot Learning by Demonstration with Dynamic Parameterization of the Orientation: An Application to Agricultural Activities

Lauretti,

Tamantini,

Tomè

et al. 2023

Robotics

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This work proposes a Learning by Demonstration framework based on Dynamic Movement Primitives (DMPs) that could be effectively adopted to plan complex activities in robotics such as the ones to be performed in agricultural domains and avoid orientation discontinuity during motion learning. The approach resorts to Lie theory and integrates into the DMP equations the exponential and logarithmic map, which converts any element of the Lie group SO(3) into an element of the tangent space so(3) and vice versa. Moreover, it includes a dynamic parameterization for the tangent space elements to manage the discontinuity of the logarithmic map. The proposed approach was tested on the Tiago robot during the fulfillment of four agricultural activities, such as digging, seeding, irrigation and harvesting. The obtained results were compared to the one achieved by using the original formulation of the DMPs and demonstrated the high capability of the proposed method to manage orientation discontinuity (the success rate was 100 % for all the tested poses).

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Section: Introductionmentioning

confidence: 99%

Robot Learning by Demonstration with Dynamic Parameterization of the Orientation: An Application to Agricultural Activities

Lauretti,

Tamantini,

Tomè

et al. 2023

Robotics

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show abstract

“…To solve this problem, researchers have proposed three types of planning algorithms: traditional algorithms, meta-heuristic algorithms, and deep learning. Among the traditional algorithms are the artificial potential field method(APF) [ 2 ], rapidly exploring random tree (RRT) [ 3 ], etc. Meta-heuristic algorithms include the ant colony algorithm [ 4 ], particle swarm algorithm [ 5 ], and gray wolf algorithm [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional continuous picking path planning based on ant colony optimization algorithm

Zhang

Wang

et al. 2023

PLoS ONE

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Fruit-picking robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology, people are demanding higher picking efficiency from fruit-picking robots. And a good fruit-picking path determines the efficiency of fruit-picking. Currently, most picking path planning is a point-to-point approach, which means that the path needs to be re-planned after each completed path planning. If the picking path planning method of the fruit-picking robot is changed from a point-to-point approach to a continuous picking method, it will significantly improve its picking efficiency. The optimal sequential ant colony optimization algorithm(OSACO) is proposed for the path planning problem of continuous fruit-picking. The algorithm adopts a new pheromone update method. It introduces a reward and punishment mechanism and a pheromone volatility factor adaptive adjustment mechanism to ensure the global search capability of the algorithm, while solving the premature and local convergence problems in the solution process. And the multi-variable bit adaptive genetic algorithm is used to optimize its initial parameters so that the parameter selection does not depend on empirical and the combination of parameters can be intelligently adjusted according to different scales, thus bringing out the best performance of the ant colony algorithm. The results show that OSACO algorithms have better global search capability, higher quality of convergence to the optimal solution, shorter generated path lengths, and greater robustness than other variants of the ant colony algorithm.

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“…It can accurately locate in a complex background environment, collect relevant power data and information, and form a complete and circular substation working system [1].Patrol robot is an important and key link in the daily work of intelligent substation, and is one of the types of special intelligent processing robots for electric power. It will patrol and monitor the environment and tasks according to the actual work needs of the substation, so as to ensure the stable implementation of power supply and dispatching [2]. The automatic obstacle avoidance and path planning of the patrol robot are the most basic built-in structures.…”

Section: Introductionmentioning

confidence: 99%

Research on automatic path planning for obstacle avoidance of intelligent substation patrol robot

Lu,

Ning,

2023

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

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Most of the current automatic planning and execution codes for the obstacle avoidance path of the patrol robot in the substation are single structure, and the planning scope is limited, which leads to the increase of the optimal time consumption of the automatic planning. Therefore, the research on the automatic planning of the obstacle avoidance path of the patrol robot in the intelligent substation is proposed. According to the actual planning requirements and standards, the basic data collection of the robot is carried out, and the obstacle avoidance operator is set. In the form of multiobjective, expand the actual planning scope, design the robot multi-objective obstacle avoidance path code, and based on this, calculate the fitness function, design an adaptive three-dimensional obstacle avoidance path automatic planning model, and use depth grid modification to achieve automatic path planning processing. The final test results show that after three stages and five independent unit areas of test comparison, the TR20 robot selected in the G intelligent substation has relatively high completion rate for the test task, and better control the optimal time consumption of automatic obstacle avoidance path planning to less than 0.5s, indicating that this kind of planning scheme will be more targeted and stable. The patrol robot can also avoid the area where obstacles appear faster and more timely, and design the shortest path, which has practical application value.

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Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method

Cited by 16 publications

References 29 publications

Robot Learning by Demonstration with Dynamic Parameterization of the Orientation: An Application to Agricultural Activities

Robot Learning by Demonstration with Dynamic Parameterization of the Orientation: An Application to Agricultural Activities

Three-dimensional continuous picking path planning based on ant colony optimization algorithm

Research on automatic path planning for obstacle avoidance of intelligent substation patrol robot

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