Multi-objective mobile robot path planning algorithm based on adaptive genetic algorithm

Yang, Changfu; Zhang, Tao; Pan, Xihao; Hu, Mengyang

doi:10.23919/chicc.2019.8865455

Cited by 10 publications

(7 citation statements)

References 11 publications

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“…(2) In order to simplify the model and facilitate the development of ideas, existing research has confirmed that the "diagonal" path can truly reflect the running situation of the vehicle, this paper assumes that the vehicle follows the "diagonal" path. [8] (3) The vehicle capacity of the delivery personnel can meet the needs of all users at the same time.…”

Section: Basic Assumptionsmentioning

confidence: 99%

A multi-constrained takeaway navigation system based on ant colony algorithm

Xu,

Duan,

Liu

2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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Section: Basic Assumptionsmentioning

confidence: 99%

A multi-constrained takeaway navigation system based on ant colony algorithm

Xu,

Duan,

Liu

2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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“…We call them path sections based on the locomotion modes that the MLR takes. d i is the Euclidean distance of each path section as shown in Equation (2) [23]:…”

Section: Optimization Objective Functionsmentioning

confidence: 99%

“…In [23], the researchers proposed a multi-objective path planning algorithm based on an adaptive genetic algorithm. In this algorithm, the self-defined genetic operator is used to realize the optimization of the path length and smoothness, and the artificial potential field theory is introduced to realize the planning of the path safety which inspired our research in the optimization of path safety.…”

Section: Introductionmentioning

confidence: 99%

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Path Planning Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy

Liu

Wang

et al. 2022

Micromachines

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The multi-locomotion robot (MLR), including bionic insect microrobot, bionic animal robot and so on, should choose different locomotion modes according to the obstacles it faces. However, under different locomotion modes, the power consumption, moving speed, and falling risk of MLR are different, and in most cases, they are mutually exclusive. This paper proposes a path planning algorithm for MLR based on a multi-objective genetic algorithm with elitist strategy (MLRMOEGA), which has four optimization objectives: power consumption, time consumption, path falling risk, and path smoothness. We propose two operators: a map analysis operator and a population diversity expansion operator, to improve the global search ability of the algorithm and solve the problem so that it is easy to fall into the local optimal solution. We conduct simulations on MATLAB, and the results show that the proposed algorithm can effectively optimize the objective function value compared with the traditional genetic algorithm under the equal weight of the four optimization objectives, and, under alternative weights, the proposed algorithm can effectively generate the corresponding path of the decision maker’s intention under the weight of preference. Compared with the traditional genetic algorithm, the global search ability is improved effectively.

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“…Conversely, several studies have been conducted to achieve multi-objective tasks. For example, genetic algorithms (Nazarahari et al, 2019;Yang et al, 2019), swarm intelligence algorithms (Hidalgo-Paniagua et al, 2015, Hidalgo-Paniagua et al, 2017, the electrostatic field approach (Bayat et al, 2018), and path planning (Ahmed and Deb, 2011;Ravankar et al, 2016) have been used to achieve multiple objectives, such as reducing the path length and increasing the safety and smoothness of trajectories. Zhu et al (Zhu et al, 2020) developed a control scheme for multi-robot collision avoidance using a vertical-ellipse-based velocity obstacle method integrated with a dynamic window approach (Fox et al, 1997).…”

mentioning

confidence: 99%

Active-sensing-based decentralized control of autonomous mobile agents for quick and smooth collision avoidance

Kano

Kanno²,

Mikami³

et al. 2022

Front. Robot. AI

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There is an increasing demand for multi-agent systems in which each mobile agent, such as a robot in a warehouse or a flying drone, moves toward its destination while avoiding other agents. Although several control schemes for collision avoidance have been proposed, they cannot achieve quick and safe movement with minimal acceleration and deceleration. To address this, we developed a decentralized control scheme that involves modifying the social force model, a model of pedestrian dynamics, and successfully realized quick, smooth, and safe movement. However, each agent had to observe many nearby agents and predict their future motion; that is, unnecessary sensing and calculations were required for each agent. In this study, we addressed this issue by introducing active sensing. In this control scheme, an index referred to as the “collision risk level” is defined, and the observation range of each agent is actively controlled on this basis. Through simulations, we demonstrated that the proposed control scheme works reasonably while reducing unnecessary sensing and calculations.

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Multi-objective mobile robot path planning algorithm based on adaptive genetic algorithm

Cited by 10 publications

References 11 publications

A multi-constrained takeaway navigation system based on ant colony algorithm

A multi-constrained takeaway navigation system based on ant colony algorithm

Path Planning Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy

Active-sensing-based decentralized control of autonomous mobile agents for quick and smooth collision avoidance

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