Optimal Trajectory Path Generation for Jointed Structure of Excavator using Genetic Algorithm

Jang, Gye-Bong; Cho, Sung-Bae

doi:10.1109/cec.2019.8790011

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Hovgard et al [23] developed an optimization approach for reducing energy consumption in multi-robot systems by determining the best execution time and order of robot motions through motion parameter modification. To acquire the optimized path planning, several heuristic-based algorithms such as neural network (NN) [24], fuzzy logic (FL) [25], and nature-inspired algorithms, including GA [26], PSO [27], and ACO [28], as well as certain Artificial Potential Field Algorithm (APFA) [29,30] and some other hybrid models [31,32], are also applied. However, many of these studies do not take energy efficiency into account.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

et al. 2022

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The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone.

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Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

et al. 2022

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“…Xu G. et al applied the improved multi-objective evolutionary algorithm (MOEA) for TriPower shovel attachment working performance optimization [8]. Jang G. et al applied the genetic algorithm (GA) on trajectory optimization of the hydraulic excavator for the optimization of energy consumption and the total length of the dig trajectory [9]. Yu X. et al optimized the design of the bucket of a hydraulic excavator by solving a multi-objective problem, achieving a light-weight and high-strength product [10].…”

Section: Introductionmentioning

confidence: 99%

Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Wang

et al. 2020

Energies

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As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of 20 s to 10 s after the first phase optimization, and the energy consumption per payload reduced by 13.28% after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

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Artificial Intelligence Technology for Path Planning of Automated Earthwork Machinery

Zhou,

Wang,

et al. 2024

Journal of Field Robotics

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The challenging characteristics of earthwork environments—complex, unstructured, and constantly evolving—pose significant challenges for the path planning of automated earthwork machinery. Recent advancements in artificial intelligence (AI) technology have opened new avenues to address these challenges, which are crucial for improving the intelligence level of automated earthwork machinery. However, there is a notable lack of comprehensive analyses on AI‐based path planning in earthwork operations. Consequently, we provide a systematic review of four AI technologies currently employed in path planning for earthwork machinery, including (1) evolutionary computation, (2) swarm intelligence, (3) machine learning, and (4) other AI‐based technologies. We analyzed the application and performance evaluation results of these technologies across various construction machinery. Through this systematic analysis, we identified several key challenges: (1) multiconstraint earthwork environments, (2) generalization across 3D unstructured sites, (3) adaptability to dynamically uncertain environments, and (4) shortage of on‐site validation. We then outline potential future directions: (1) integration of generative AI with reinforcement learning, (2) use of large model technology, (3) adoption of embodied intelligence technology, and (4) conduction of more on‐site experiments.

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Optimal Trajectory Path Generation for Jointed Structure of Excavator using Genetic Algorithm

Cited by 3 publications

References 22 publications

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Artificial Intelligence Technology for Path Planning of Automated Earthwork Machinery

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