Attitude Control of Multi-motion Mode Spherical Probe Robots Based on Decoupled Dynamics

Ma, Wenke; Chang, Chen; Cao, Yanlong; Wang, Futao; Wang, Pengfei; Li, Bingyang

doi:10.1007/978-981-19-6613-2_565

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…There are few studies on the postures control and trajectory tracking of spherical robots. The primary control algorithms used include the sliding mode algorithm [16][17][18], the PID algorithm [19], the fuzzy algorithm [20,21], and others [22,23]. Kayacan decoupled the spherical robot into two subsystems and designed a feedback controller with fuzzy logic to achieve linear motion [24].…”

Section: Introductionmentioning

confidence: 99%

Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning

Cao³

et al. 2023

Applied Sciences

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As deep space exploration tasks become increasingly complex, the mobility and adaptability of traditional wheeled or tracked probe robots with high functional density are constrained in harsh, dangerous, or unknown environments. A practical solution to these challenges is designing a probe robot for preliminary exploration in unknown areas, which is characterized by robust adaptability, simple structure, light weight, and minimal volume. Compared to the traditional deep space probe robot, the spherical robot with a geometric, symmetrical structure shows better adaptability to the complex ground environment. Considering the uncertain detection environment, the spherical robot should brake rapidly after jumping to avoid reentering obstacles. Moreover, since it is equipped with optical modules for deep space exploration missions, the spherical robot must maintain motion stability during the rolling process to ensure the quality of photos and videos captured. However, due to the nonlinear coupling and parameter uncertainty of the spherical robot, it is tedious to adjust controller parameters. Moreover, the adaptability of controllers with fixed parameters is limited. This paper proposes an adaptive proportion–integration–differentiation (PID) control method based on reinforcement learning for the multi-motion mode spherical probe robot (MMSPR) with rolling and jumping. This method uses the soft actor–critic (SAC) algorithm to adjust the parameters of the PID controller and introduces a switching control strategy to reduce static error. As the simulation results show, this method can facilitate the MMSPR’s convergence within 0.02 s regarding motion stability. In addition, in terms of braking, it enables an MMSPR with random initial speed brake within a convergence time of 0.045 s and a displacement of 0.0013 m. Compared with the PID method with fixed parameters, the braking displacement of the MMSPR is reduced by about 38%, and the convergence time is reduced by about 20%, showing better universality and adaptability.

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Section: Introductionmentioning

confidence: 99%

Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning

Cao³

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

Attitude Control of Multi-motion Mode Spherical Probe Robots Based on Decoupled Dynamics

Cited by 1 publication

References 18 publications

Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning

Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning

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