About this Research Topic
The escalating presence of robots in diverse industries can be attributed to their integration into progressively intricate processes. This surge prompts designers to spearhead innovations in robot architectures, prioritizing enhanced robustness, sophisticated algorithms, and the integration of artificial intelligence. Within the realm of robotics, redundant robots stand out as an advanced category characterized by an augmented number of degrees of freedom (DOF) in comparison to their non-redundant counterparts. In contrast to non-redundant robots, which possess just enough DOF for a specific task, redundant robots boast additional joints and articulations, providing notable advantages. These advantages encompass graceful obstacle navigation, heightened adaptability to dynamic environments, improved task precision, and enhanced safety in collaboration with humans. Despite their heightened utility across diverse applications, the control and kinematic solutions for redundant robots pose increased complexity due to the existence of multiple feasible configurations. This technological leap underscores the versatility and potential of redundant robots across various domains. Redundancy in redundant robots adds complexity to the kinematic or dynamic problem, as there may be multiple possible solutions, and the choice of the appropriate solution may require additional considerations, such as optimization of specific criteria. The demand for robotic systems is on the rise, but there exists a shortage of artificial intelligence algorithms that can effectively support the kinematics, dynamics, and control of these robots. The available proposals for new morphologies are currently limited and require expansion to enable the integration of these robots into a broader range of industries.
This Research Topic aims to address this gap in algorithm development and explore innovative morphological designs, which are crucial steps in meeting the growing need for redundant robots and enhancing their applicability across various sectors. The primary objectives include developing advanced AI algorithms to support the kinematics, dynamics, and control of redundant robots, as well as proposing new morphological designs that can be integrated into a broader range of industries. Specific questions to be answered include: What are the most effective AI algorithms for controlling redundant robots? How can new morphological designs improve the adaptability and precision of redundant robots in various applications?
To gather further insights into the design and application of redundant robots, we welcome articles addressing, but not limited to, the following themes:
- Identification of operational needs and requirements for redundant robots: surveys or original research focused on identifying and justifying operational requirements for various medical, agricultural, and industrial applications.
- Engineering and computational designs tailored to operational needs for redundant robots, encompassing structural design and modeling, material selection and synthesis, actuation modalities, sensing principles, sensor-free estimators, control systems, decision making, and artificial intelligence integration.
- Innovation in new applications of redundant robots for commercial, medical, agricultural, and industrial applications.
- Developing new actuators or sensors to develop better stability and robustness to redundant robot morphologies.
- Designing risk-aware redundant robots and integrating risk awareness using computer vision, machine learning, deep learning, or others.
- Development of validation studies specifically tailored for redundant robots, addressing the unique challenges associated with their increased degrees of freedom and complex kinematics.
- Evaluation of redundant robots, including performance assessments, adoption in different operational settings, exploration of future challenges, and new morphologies or architectures.
This Research Topic aims to address this gap in algorithm development and explore innovative morphological designs, which are crucial steps in meeting the growing need for redundant robots and enhancing their applicability across various sectors. The primary objectives include developing advanced AI algorithms to support the kinematics, dynamics, and control of redundant robots, as well as proposing new morphological designs that can be integrated into a broader range of industries. Specific questions to be answered include: What are the most effective AI algorithms for controlling redundant robots? How can new morphological designs improve the adaptability and precision of redundant robots in various applications?
To gather further insights into the design and application of redundant robots, we welcome articles addressing, but not limited to, the following themes:
- Identification of operational needs and requirements for redundant robots: surveys or original research focused on identifying and justifying operational requirements for various medical, agricultural, and industrial applications.
- Engineering and computational designs tailored to operational needs for redundant robots, encompassing structural design and modeling, material selection and synthesis, actuation modalities, sensing principles, sensor-free estimators, control systems, decision making, and artificial intelligence integration.
- Innovation in new applications of redundant robots for commercial, medical, agricultural, and industrial applications.
- Developing new actuators or sensors to develop better stability and robustness to redundant robot morphologies.
- Designing risk-aware redundant robots and integrating risk awareness using computer vision, machine learning, deep learning, or others.
- Development of validation studies specifically tailored for redundant robots, addressing the unique challenges associated with their increased degrees of freedom and complex kinematics.
- Evaluation of redundant robots, including performance assessments, adoption in different operational settings, exploration of future challenges, and new morphologies or architectures.
Keywords: kinematics, dynamics, neural networks, machine learning, morphologies
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
