Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

Krämer, Maximilian; Bertram, Torsten

doi:10.1109/access.2022.3159233

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…Numerous factors affect how energy is recovered, so the effect of energy saving is unclear. Conventional palletizing robots usually employ the strategies of best time [ 6 , 7 ] and local optimization [ 8 ]. Those approaches can save energy consumption by reducing the running time.…”

Section: Introductionmentioning

confidence: 99%

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

Deng

Zhou

Zhao

et al. 2022

Sensors

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Energy saving in palletizing robot is a fundamental problem in the field of industrial robots. However, the palletizing robot often suffers from the problems of high energy consumption and lacking flexibility. In this work, we introduce a novel differential evolution algorithm to address the adverse effects caused by the instability of the initial trajectory parameters while reducing the energy. Specially, a simplified analytical model of the palletizing robot is firstly developed. Then, the simplified analytical model and the differential evolutionary algorithm are combined to form a planner with the goal of reducing energy consumption. The energy saving planner optimizes the initial parameters of the trajectories collected by the bionic demonstration system, which in turn enables a reduction in the operating power consumption of the palletizing robot. The major novelty of this article is the use of a differential evolutionary algorithm that can save the energy consumption as well as boosting its flexibility. Comparing with the traditional algorithms, the proposed method can achieve the state-of-the-art performance. Simulated and actual experimental results illustrate that the optimized trajectory parameters can effectively reduce the energy consumption of palletizing robot by 16%.

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Section: Introductionmentioning

confidence: 99%

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

Deng

Zhou

Zhao

et al. 2022

Sensors

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“…Optimization algorithms are classified into global and local optimization techniques according to the required solution level. The global optimization technique [21] aims at finding the best solution in the entire search area even though it takes time to process, whereas the local optimization technique [22] aims at finding the best solution in a partial search area within a short time. There are various methodologies for optimization techniques, and the representative methods are genetic algorithm (GA) and particle swarm optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

A Design and Optimization of a CGK-Based Fuzzy Granular Model Based on the Generation of Rational Information Granules

Yeom

Kwak

2022

Applied Sciences

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This study proposes an optimized context-based Gustafson Kessel (CGK)-based fuzzy granular model based on the generation of rational information granules and an optimized CGK-based fuzzy granular model with the aggregated structure. The conventional context-based fuzzy-c-means (CFCM) clustering generates clusters considering the input and output spaces. However, the prediction performance decreases when the specific data points with geometric features are used. The CGK clustering solves the above situation by generating valid clusters considering the geometric attributes of data in input and output spaces with the aid of the Mahalanobis distance. However, it is necessary to generate rational information granules (IGs) because there is a significant change in performance according to the context generated in the output space and the shape, size, and several clusters generated in the input space. As a result, the rational IGs are obtained by considering the relationship between the coverage and specificity of IG using the genetic algorithm (GA). Thus, the optimized CGK-based fuzzy granular models with the aggregated structure are designed based on rational IGs. The prediction performance was compared using the two databases to verify the validity of the proposed method. Finally, the experiments revealed that the performance of the proposed method is higher than that of the previous model.

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“…The study of trajectory planning can be divided into two parts: global planning and local planning, where local trajectory planning is the key to trajectory planning ( 6 ). Traditional planning methods include graph search, artificial potential fields, space sampling, and numerical optimization ( 7 – 9 ).…”

mentioning

confidence: 99%

Trajectory Planning Framework Combining Replanning and Hierarchical Planning

Qin,

Liu,

Shang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To enable autonomous vehicles to respond quickly to changes in the surrounding environment and to generate safe and stable trajectory, we propose a safety-based hierarchical trajectory planning method that divides the planning module into two parts: the planner and the replanning monitor. In the planner, we first propose a fast linear trajectory planning method that uses the mass point model instead of the vehicle model and linearizes the collision avoidance constraint using the Big-M method for linear programming (Big-M) to obtain a linear programming model. The complete vehicle kinematic model is then built in the nonlinear programming stage, the collision constraints and cost functions are refined, and the rough solution of the linear programming is brought into it to obtain the exact solution. The replanning monitor is divided into safety and comfort monitors. The safety monitor will always pay attention to the changes in the surrounding environment and calculate whether the vehicle is in danger of collision in the future period, while the comfort monitor is more concerned with the comfort of driving the vehicle; when the monitor requirements are not met, replanning will be carried out. By simulating the driving environment, the proposed algorithm can form a safe and comfortable trajectory, which verifies the rationality of the proposed method.

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Improving Local Trajectory Optimization by Enhanced Initialization and Global Guidance

Cited by 6 publications

References 24 publications

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

A Design and Optimization of a CGK-Based Fuzzy Granular Model Based on the Generation of Rational Information Granules

Trajectory Planning Framework Combining Replanning and Hierarchical Planning

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