Robots Solving the Urgent Problems by Themselves: A Review

Guo, Dongdong; Fu, Li; Wang, Lingling

doi:10.1109/icce-tw46550.2019.8991947

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Bio-inspired control architectures aim at mimicking the process of generation of locomotion through the implementation of an ANN [37]. Their implementation intends to provide an optimal adaptation of the behavior of the hexapod to the environment [6,38].…”

Section: Bio-inspired Controllersmentioning

confidence: 99%

Trends in the Control of Hexapod Robots: A Survey

et al. 2021

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The static stability of hexapods motivates their design for tasks in which stable locomotion is required, such as navigation across complex environments. This task is of high interest due to the possibility of replacing human beings in exploration, surveillance and rescue missions. For this application, the control system must adapt the actuation of the limbs according to their surroundings to ensure that the hexapod does not tumble during locomotion. The most traditional approach considers their limbs as robotic manipulators and relies on mechanical models to actuate them. However, the increasing interest in model-free models for the control of these systems has led to the design of novel solutions. Through a systematic literature review, this paper intends to overview the trends in this field of research and determine in which stage the design of autonomous and adaptable controllers for hexapods is.

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Section: Bio-inspired Controllersmentioning

confidence: 99%

Trends in the Control of Hexapod Robots: A Survey

et al. 2021

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“…If a fault is identified, a pre-engineered alternative control scheme is invoked to mitigate the issue. However, the effectiveness of these strategies is intrinsically tied to the designer's comprehensive understanding of the robot's mechanical intricacies, often necessitating meticulous manual adjustments [15]. Moreover, the rigidity of relying on predefined alternative control schemes becomes a significant constraint in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

“…This paradigm empowers robots to acquire emergency-response skills independently [15], reducing the dependence on domain knowledge when designing algorithms to some extent [1,24]. Deep Reinforcement Learning (DRL) has gained prominence in quadruped robot control due to its ability to learn nonlinear control strategies [26].…”

Section: Introductionmentioning

confidence: 99%