A Novel Resilient Robot: Kinematic Analysis and Experimentation

Wang, Fei; Qian, Zhiqin; Yan, Zhiguang; Yuan, Chenwang; Zhang, Wenjun

doi:10.1109/access.2019.2962058

Cited by 81 publications

(31 citation statements)

References 21 publications

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“…Apart from optimizations of motion planning algorithms, hardware planning may be a possible direction to improve the performance of future motion planning system. Hardware planning refers to the reconfiguration and adjustment of hardware of robotic system, therefore robots can be reconfigured into different shapes (Salemi et al, 2006;Wang et al, 2020) to improve their performance in motion planning. Decision: Traditional algorithms (e.g., A*) feature the global trajectory planning, while optimal value RL and policy gradient RL feature the safe and quick motion planning locally.…”

Section: Convergence Stability Tablementioning

confidence: 99%

A review of motion planning algorithms for intelligent robots

2021

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Principles of typical motion planning algorithms are investigated and analyzed in this paper. These algorithms include traditional planning algorithms, classical machine learning algorithms, optimal value reinforcement learning, and policy gradient reinforcement learning. Traditional planning algorithms investigated include graph search algorithms, sampling-based algorithms, interpolating curve algorithms, and reaction-based algorithms. Classical machine learning algorithms include multiclass support vector machine, long short-term memory, Monte-Carlo tree search and convolutional neural network. Optimal value reinforcement learning algorithms include Q learning, deep Q-learning network, double deep Q-learning network, dueling deep Q-learning network. Policy gradient algorithms include policy gradient method, actor-critic algorithm, asynchronous advantage actor-critic, advantage actor-critic, deterministic policy gradient, deep deterministic policy gradient, trust region policy optimization and proximal policy optimization. New general criteria are also introduced to evaluate the performance and application of motion planning algorithms by analytical comparisons. The convergence speed and stability of optimal value and policy gradient algorithms are specially analyzed. Future directions are presented analytically according to principles and analytical comparisons of motion planning algorithms. This paper provides researchers with a clear and comprehensive understanding about advantages, disadvantages, relationships, and future of motion planning algorithms in robots, and paves ways for better motion planning algorithms in academia, engineering, and manufacturing.

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Section: Convergence Stability Tablementioning

confidence: 99%

A review of motion planning algorithms for intelligent robots

2021

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“…Similarly to this article, the metacontrol approach implemented here uses self-models to recover from damage. Another example of self-models usage is [ 10 ], which employs self-assembly and reconfiguration on modular robots. In this case, they use the function-context-behavior-principle-state-structure (FCBPSS) model [ 11 ], which is a semantic framework to define the system domain model.…”

Section: State Of the Artmentioning

confidence: 99%

Functional Self-Awareness and Metacontrol for Underwater Robot Autonomy

Aguado

Milosevic

Hernández

et al. 2021

Sensors

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Autonomous systems are expected to maintain a dependable operation without human intervention. They are intended to fulfill the mission for which they were deployed, properly handling the disturbances that may affect them. Underwater robots, such as the UX-1 mine explorer developed in the UNEXMIN project, are paradigmatic examples of this need. Underwater robots are affected by both external and internal disturbances that hamper their capability for autonomous operation. Long-term autonomy requires not only the capability of perceiving and properly acting in open environments but also a sufficient degree of robustness and resilience so as to maintain and recover the operational functionality of the system when disturbed by unexpected events. In this article, we analyze the operational conditions for autonomous underwater robots with a special emphasis on the UX-1 miner explorer. We then describe a knowledge-based self-awareness and metacontrol subsystem that enables the autonomous reconfiguration of the robot subsystems to keep mission-oriented capability. This resilience augmenting solution is based on the deep modeling of the functional architecture of the autonomous robot in combination with ontological reasoning to allow self-diagnosis and reconfiguration during operation. This mechanism can transparently use robot functional redundancy to ensure mission satisfaction, even in the presence of faults.

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“…In Wang et al (2019), 91 the authors presented resilience in light of its implication to robots. In this case, resilient robot is a "robot that can recover its function after the robot is partially damaged."…”

Section: Resilience Conceptmentioning

confidence: 99%

“…In Wang et al (2019), 91 the authors presented resilience in light of its implication to robots. In this case, resilient robot is a “ robot that can recover its function after the robot is partially damaged .” In Zhang and Lin (2014), 92 the authors gave more general definition of resilience applied to engineering.…”

Section: Resilience Conceptmentioning

confidence: 99%

Sustainable performance management using resilience engineering

Bouloiz

2020

International Journal of Engineering Business Management

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Several efforts have focused on the continuous improvement of firm performance, but many disturbances still occur because of various random failures. Resilience which is the ability to withstand shock and maintain critical function, has been recognized as an important approach to keep a firm operating in varying conditions, even if these conditions are excepted or not. With the objective to further enhance firm performance and be part of a sustainable development, it is necessary to study this performance through the resilience in order to manage all disturbances. The objective of this work is to propose a specific management model able to define the functions and barriers that contribute to the resilience of sustainable performance of firm. In this paper, the methodology is explained in detail, and its application trough textile firm is discussed.

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A Novel Resilient Robot: Kinematic Analysis and Experimentation

Cited by 81 publications

References 21 publications

A review of motion planning algorithms for intelligent robots

A review of motion planning algorithms for intelligent robots

Functional Self-Awareness and Metacontrol for Underwater Robot Autonomy

Sustainable performance management using resilience engineering

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