Motion planning with adaptive motion primitives for modular robots

Vonásek, Vojtĕch; Saska, Martin; Košnar, Karel; Přeučil, Libor

doi:10.1016/j.asoc.2015.05.002

Cited by 6 publications

(2 citation statements)

References 51 publications

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“…Several authors have investigated how to combine/enhance RRT with PSO. Vonasek et al introduced RRT-AMP, which uses PSO to adapt motion primitives for robot locomotion [75]. In their approach, PSO is used to optimize the parameters governing the driving primitives, thereby improving the control of the robot's movement.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Using Combined Informed Rapidly- Exploring Random Tree Star and Particle Swarm Optimization Algorithms

Pohan,

Trilaksono,

Santosa

et al. 2024

IEEE Access

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This study proposes a path planning algorithm that combines an informed rapidly-exploring random tree (RRT*) and particle swarm optimization (PSO) algorithms. The informed RRT* algorithm can build optimal pathways quickly. However, in some cases, the informed RRT* strategy cannot help increase the convergence rate. Meanwhile, the PSO algorithm can be combined with other algorithms to improve algorithm performance. The proposed algorithm will combine the advantages of informed RRT* and PSO algorithms. The proposed algorithm is called the RRT-PSO algorithm. The RRT-PSO algorithm's performance was compared to the informed RRT*, RRT*, RRT-ACS, and informed RRT*-connect algorithms using eight benchmark scenarios. The test results on benchmark scenarios with known optimal values demonstrate that the RRT-PSO algorithm achieves those optimal values. The statistical analysis shows that the RRT-PSO algorithm has a fast convergence rate compared to other comparison algorithms. The test results also demonstrate the RRT-PSO algorithm's stability and robustness compared to other comparison algorithms. Our test results also show the RRT-PSO algorithm's advantages over other PSObased and traditional path planning algorithms. The results show that the RRT-PSO algorithm is suitable for applications requiring a fast and optimal path planning algorithm, such as self-driving cars and unmanned aerial vehicles (UAV).INDEX TERMS Path planning, convergence rate, optimal path, informed rapidly-exploring random tree, particle swarm optimization.

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Section: Introductionmentioning

confidence: 99%

Path Planning Using Combined Informed Rapidly- Exploring Random Tree Star and Particle Swarm Optimization Algorithms

Pohan,

Trilaksono,

Santosa

et al. 2024

IEEE Access

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show abstract

“…14 Previous researchers mainly focused on the structure design [12][13][14] and the locomotion and control. 6,7,9,[15][16][17] However, the research of automatic generation of motion equations is not enough, and especially the configuration adaptive control is less.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling and configuration adaptive control for modular reconfigurable robot

Zhang

Liu

2017

Advances in Mechanical Engineering

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A unit module of the mobile modular reconfigurable robot (M 2 rBot) with the 12 connection ways is first introduced. For expressing the spatial connection relations and the traversing paths of the M 2 rBot, a novel method of the automatic generation of the spatial connection matrix is presented based on our proposed double-module space pose transformation. The motion equations are also automatically generated to solve the reconstruction problems of mechanical configuration, kinematics, and dynamics. This method enables to analyze the kinematics and dynamics for both serial and multi-branch modular robots with different configurations. As for the control strategies, a fuzzy robust adaptive controller is also developed to adapt to the different configurations without adjusting any control parameters. The results show that the novel method is effective, and the proposed controller can satisfy tracking performances and quickly adapt to the change of the configurations, the frictions, and the external disturbances. The proposed method and controller are easy to extend to other modular robots.

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Learning directed locomotion in modular robots with evolvable morphologies

Lan

Carlo

Diggelen

et al. 2021

Applied Soft Computing

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Motion planning with adaptive motion primitives for modular robots

Cited by 6 publications

References 51 publications

Path Planning Using Combined Informed Rapidly- Exploring Random Tree Star and Particle Swarm Optimization Algorithms

Path Planning Using Combined Informed Rapidly- Exploring Random Tree Star and Particle Swarm Optimization Algorithms

Dynamic modeling and configuration adaptive control for modular reconfigurable robot

Learning directed locomotion in modular robots with evolvable morphologies

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