The development of a versatile, fully-capable humanoid robot as envisioned in science fiction books is one of the most challenging but interesting issues in the robotic field. Currently, existing humanoid robots are designed with different purposes and applications in mind. In humanoid robot development process, each robot is designed with various characteristics, abilities, and equipment, which influence the general structure, cost, and difficulty of development. Even though humanoid robot development is very popular, a few review papers are focusing on the design and development process of humanoid robots. Motivated by this, we present this review paper to show variations in the requirements, design, and development process and also propose a taxonomy of existing humanoid robots. It aims at demonstrating a general perspective of existing humanoid robots’ characteristics and applications. This paper includes state-of-the-art and successfully reported existing humanoid robot designs along with different robots used in various robot competitions.

A comprehensive survey on humanoid robot development

This paper provides a comprehensive review of the current status, advancements, and future prospects of humanoid robots, highlighting their significance in driving the evolution of next-generation industries. By analyzing various research endeavors and key technologies, encompassing ontology structure, control and decision-making, and perception and interaction, a holistic overview of the current state of humanoid robot research is presented. Furthermore, emerging challenges in the field are identified, emphasizing the necessity for a deeper understanding of biological motion mechanisms, improved structural design, enhanced material applications, advanced drive and control methods, and efficient energy utilization. The integration of bionics, brain-inspired intelligence, mechanics, and control is underscored as a promising direction for the development of advanced humanoid robotic systems. This paper serves as an invaluable resource, offering insightful guidance to researchers in the field, while contributing to the ongoing evolution and potential of humanoid robots across diverse domains. 

Advancements in Humanoid Robots: A Comprehensive Review and Future Prospects

Deep learning has revolutionised many fields, but it is still challenging to transfer its success to small mobile robots with minimal hardware. Specifically, some work has been done to this effect in the RoboCup humanoid football domain, but results that are performant and efficient and still generally applicable outside of this domain are lacking. We propose an approach conceptually different from those taken previously. It is based on semantic segmentation and does achieve these desired properties. In detail, it is being able to process full VGA images in real-time on a low-power mobile processor. It can further handle multiple image dimensions without retraining, it does not require specific domain knowledge to achieve a high frame rate and it is applicable on a minimal mobile hardware.

/pdf/deep-learning-for-semantic-segmentation-on-minimal-hardware-53uh8oue48.pdf

Deep Learning for Semantic Segmentation on Minimal Hardware

Cobotic workstations in industrial plants involve a new kind of collaborative robots that can interact with operators. These cobots enable more flexibility and they can reduce the cycle time and the floor space of the workstation. However, cobots also introduce new safety concerns with regard to operators, and they may have an impact on the workstation ergonomics. For those reasons, introducing cobots in a workstation always require additional studies on safety and ergonomics before being applied and certified. Certification rules are often complex to understand. We present the SEEROB framework for the Safety and Ergonomics Evaluation of ROBotic workstations. The SEEROB framework simulates a physics-based digital twin of the cobotic workstation and computes a large panel of criteria used for safety and ergonomics. These criteria may be processed for the certification of the workstation. The SEEROB framework also uses extended reality technologies to display the digital twin and its associated data: users can use virtual reality headsets for the design of non-existing workstations, and mixed reality devices to better understand safety and ergonomics constraints on existing workstations. The SEEROB framework was tested on various laboratory and industrial use cases, involving different kinds of robots.

/pdf/using-physics-based-digital-twins-and-extended-reality-for-1ifrf77q.pdf

Using Physics-Based Digital Twins and Extended Reality for the Safety and Ergonomics Evaluation of Cobotic Workstations

In 2019, Rhoban Football Club reached the first place of the KidSize soccer competition for the fourth time and performed the first in-game throw-in in the history of the Humanoid league. Building on our existing code-base, we improved some specific functionalities, introduced new behaviors and experimented with original methods for labeling videos. This paper presents and reviews our latest changes to both software and hardware, highlighting the lessons learned during RoboCup.

https://hal.archives-ouvertes.fr/hal-02330530/document

Rhoban Football Club: RoboCup Humanoid KidSize 2019 Champion Team Paper

Field Oriented Control (FOC) is a well-known strategy for controlling different types of electrically commutated motors (e.g., aternating current based, brushless DC, etc.) via phase-commutation. As opposed to the widely used brushed DC motors, which are by design mechanically commutated, brushless motors (e.g., BLDC, stepper, AC, etc.) rely on the control algorithms and electronics to create the appropriate magnetic fields and ensure motors’ desired motion. Many techniques (Bida et al., 2018; Jalili, 2009) have been developed over the years, of which FOC is arguably one of the most efficient ones. However, the FOC approach has relatively complex control architecture (see Figure 1) requiring substantial computational performance. Since the motion control applications are implemented on embedded systems with limited capabilities, various optimizations and simplifications are usually necessary. This makes the FOC implementations almost exclusively specific to certain microcontroller architectures (Belhamel et al., 2020; Cheles & Sammoud, 2008), motor drivers, current and position sensors, and motors (Carey et al., 2019; Castiglia et al., 2018; Reddy & Murali, 2016).

SimpleFOC: A Field Oriented Control (FOC) Library for Controlling Brushless Direct Current (BLDC) and Stepper Motors

Ellipsoid-based manipulability measures are often used to characterize the force/velocity task-space capabilities of robots. While computationally simple, this approach largely approximate and underestimate the true capabilities. Force/velocity polytopes appear to be a more appropriate representation to characterize the robot’s task-space capabilities. However, due to the computational complexity of the associated vertex search problem, the polytope approach is mostly restricted to offline use, e.g. as a tool aiding robot mechanical design, robot placement in work-space and offline trajectory planning. In this paper, a novel on-line polytope vertex search algorithm is proposed. It exploits the parallelotope geometry of actuator constraints. The proposed algorithm significantly reduces the complexity and computation time of the vertex search problem in comparison to commonly used algorithms. In order to highlight the on-line capability of the proposed algorithm and its potential for robot control, a challenging experiment with two collaborating Franka Emika Panda robots, carrying a load of 12 kilograms, is proposed. In this experiment, the load distribution is adapted on-line, as a function of the configuration dependant task-space force capability of each robot, in order to avoid, as much as possible, the saturation of their capacity.

https://hal.archives-ouvertes.fr/hal-02993408/document

On-line force capability evaluation based on efficient polytope vertex search

Ellipsoid-based manipulability measures are often used to characterize the force/velocity task-space capabilities of robots. While computationally simple, this approach largely approximate and underestimate the true capabilities. Force/velocity polytopes appear to be a more appropriate representation to characterize robot's task-space capabilities. However, due to the computational complexity of the associated vertex search problem, the polytope approach is mostly restricted to offline use, e.g. as a tool aiding robot mechanical design, robot placement in work-space and offline trajectory planning. In this paper, a novel on-line polytope vertex search algorithm is proposed. It exploits the parallelotop geometry of actuator constraints. The proposed algorithm significantly reduces the complexity and computation time of the vertex search problem in comparison to commonly used algorithms. In order to highlight the on-line capability of the proposed algorithm and its potential for robot control, a challenging experiment with two collaborating Franka Emika Panda robots, carrying a load of 12 kilograms, is proposed. In this experiment, the load distribution is adapted on-line, as a function of the configuration dependant task-space force capability of each robot, in order to avoid, as much as possible, the saturation of their capacity.

Rhoban Football Club: RoboCup Humanoid KidSize 2019 Champion Team Paper.

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SimpleFOC: A Field Oriented Control (FOC) Library for Controlling Brushless Direct Current (BLDC) and Stepper Motors

Rhoban Football Club: RoboCup Humanoid KidSize 2019 Champion Team Paper

On-line force capability evaluation based on efficient polytope vertex search

On-line force capability evaluation based on efficient polytope vertex search

Rhoban Football Club: RoboCup Humanoid KidSize 2019 Champion Team Paper.