Four-legged Robot Research Articles

Assessing proxemics impact on Human-Robot collaboration safety in construction: A virtual reality study with four-legged robots

Recent advancements in four-legged robots have prompted their integration into the construction industry, yet the safety implications of their deployment remain inadequately explored. As such comprehensive investigations are required to ensure the safety of robot deployment and the well-being of construction professionals who work with and alongside these robots. This study addresses this gap by conducting a user-centered experiment employing virtual reality to assess human behavior and safety impacts in varying interaction spaces with four-legged robots within a simulated construction environment. By employing objective and subjective measures, including physiological and attentional responses, emotional reactions, situational awareness, risk perceptions, and attitudes towards robots, this study analyzes the impact of proxemics on construction individuals at two distinct interaction spaces: proximal (1.5 – 4 ft) and distal (12 – 25 ft) from the four-legged robots. The study found that while participants’ physiological responses, emotional states, situational awareness, risk perceptions, and attitudes towards robots were not significantly influenced by four-legged robot interaction space, those in the distal group allocated significantly more attention to the robot, particularly in terms of fixation count, indicating a significant proxemics impact on attentional states. These findings shed light on the safety implications of human-robot collaboration on jobsites, contributing to the advancement of safe and efficient practices in construction settings.

Research on designing dynamic model of TITAN-VIII quadruped robot based on the robot structure

Quadruped robots have many advantages over wheeled robots in that they have high mobility and can move on any terrain. However, the control problem of four-legged robots will be more complicated because the robot's kinematic model has a high order and the dynamic process is complicated, so it is necessary to implement order reduction methods when constructing kinematic equations for four-legged robots. The article proposes a simplified method to determine the kinematic equations for four-legged robots called TITAN-VIII. The Denavit-Hartenberg (D-H) rule is used to analyze the forward kinematics. The inverse kinematic equations are determined on the basis of mathematics and the structure of the robot's legs. The research results are simulated on MATLAB Simulink software.

Embedded ML-based locomotion control for a 12-joint four-legged robot

Reinforcement learning has developed as a promising approach for robot locomotion control, which can save engineering effort compared to conventional approaches. This article presents the implementation of Reinforcement learning on a low-cost, 12 degree of freedom robot known as a quadruped (spider) to optimize locomotion control, enabling the robot to move on different surfaces such as flat surfaces, ramps, speed bumps, and rough terrain. A MATLAB Simulink model is developed as a digital twin of the spider robot. The dynamics of the model were studied and validated using an open-loop algorithm. Then, the model is utilized in a training simulation environment to apply the reinforcement learning algorithm, showing its ability to move along a predefined path as a replacement for conventional motion control systems. Moreover, the work compares the performance and effectiveness of machine learning-based locomotion control with traditional motion control systems regarding navigation accuracy, speed, and adaptability in challenging environments. The minimum hardware requirements are also studied to move the experiment from simulation to reality.

Gait planning based on bionic quadruped robot

Based on the ankylosaurus, a four-legged robot structure with 14 degrees of freedom was designed in this study. The kinematic model was established using the Denavit-Hartenberg (D-H) method. The inverse kinematics of the robot was analyzed, and the angle equations of each leg joint were obtained. In order to reduce the kinetic energy generated when leaving the ground and landing, the compound cycloid was modified. The modified foot curve effectively reduces energy and meets the kinematic requirements. On the basis of the foot trajectory, the diagonal leg phase difference was set to 0.5, and the diagonal gait was adopted as the gait of the bionic quadrupled robot. Simulink software was used to construct the simulation environment, and the maximum error of the simulation results and the theoretical step height was 2.05%, and the step length maximum error was 2.39%. During walking, the thigh joint angle was −92.82° to −80.21°, and the hip joint angle was 22.23° to 43.83°. Compared with the simulation trajectory and the theoretical curve, because the experiment did not consider the balance of the fuselage, there was a certain error when the leg was raised to the highest point. Overall, the gait planning strategy designed in this study basically achieves the expected effect, lays a certain foundation for the practical application of the bionic quadruped dinosaur robot, and also provides a reference for the subsequent research.

ANYmal parkour: Learning agile navigation for quadrupedal robots.

Performing agile navigation with four-legged robots is a challenging task because of the highly dynamic motions, contacts with various parts of the robot, and the limited field of view of the perception sensors. Here, we propose a fully learned approach to training such robots and conquer scenarios that are reminiscent of parkour challenges. The method involves training advanced locomotion skills for several types of obstacles, such as walking, jumping, climbing, and crouching, and then using a high-level policy to select and control those skills across the terrain. Thanks to our hierarchical formulation, the navigation policy is aware of the capabilities of each skill, and it will adapt its behavior depending on the scenario at hand. In addition, a perception module was trained to reconstruct obstacles from highly occluded and noisy sensory data and endows the pipeline with scene understanding. Compared with previous attempts, our method can plan a path for challenging scenarios without expert demonstration, offline computation, a priori knowledge of the environment, or taking contacts explicitly into account. Although these modules were trained from simulated data only, our real-world experiments demonstrate successful transfer on hardware, where the robot navigated and crossed consecutive challenging obstacles with speeds of up to 2 meters per second.

Преодоление шагающим роботом препятствий, характерных для равнины

An upper estimate of the maximum width of the forbidden zone for foot fulcrums, which a walking robot can overcome in static stability mode, is presented. Using the examples of six-legged and four-legged robot, it is shown that the obtained estimate can't be improved. For this purpose, the sequences of the robot's foot placement have been formed, ensuring the achievement of the estimation meaning. The results of computer modeling and video materials illustrating the process of overcoming an obstacle by a six-legged robot are presented.

Interactive Quadruped Felid Class Robot with a Neural Processing Unit

The authors consider the urgent task of developing bionic robots, in particular robots on four legs. Their advantages are the ability to move on uneven terrain, to perform reconnaissance, rescue and other dangerous work, where they could replace humans. A review of the existing best-known and most functional bionic four-legged robots is given, with descriptions of their strengths and weaknesses, as well as peculiarities of their movement and use. The main problems in the development of such devices and their control systems are highlighted. The article provides information on the research and development of an interactive bionic robot of the felid class, whose skeletal structure control implementation is deeply explored. The features of hardware and software implementation of the robot are considered, and schematic and real images of the construction are presented. The application of a microcomputer device with a neural processing unit to solve the problem of machine vision is highlighted. The results of testing machine vision using the Yolo3 neural network in streaming video mode are presented. The average accuracy of the open face recognition as a result of the tests was 95 %. For different degrees of occlusion, the average score was 80 %, and occlusion variants in which the neural network was unable to recognize faces were also identified. The article concludes with a discussion of the advantages and disadvantages of the proposed robot and the possibility of its application in human life, including the solution of various practical tasks.

Platform-independent visual installation progress monitoring for construction automation

Efficient interior progress monitoring is crucial for the timely completion of construction projects. Although robots have been used for data acquisition to automate interior progress monitoring, existing methods do not adequately consider the variations of robot platforms and different types of construction environments, which makes it challenging to apply these methods to various robots and environments. This paper proposes an integrated system that achieves automated interior installation progress monitoring, which can be applied to various construction environments and robot platforms. Algorithms are proposed to systematically generate navigation goal points for robot navigations based on BIM information of objects, which enables the detection of target objects with the proper viewing distance and angle. A transformer-based object detector is used to recognize the installation status of building elements, and a progress updating module is developed to correlate the detection results with the robot’s sensory and BIM information to generate a construction progress report for interior installation. This framework hierarchically estimates the percentage of project completion and allows for tracking of the installation work progress. The proposed system has been verified through laboratory and onsite experiments using various platforms, including a mobile robot, a four-legged robot, a drone, and a smartphone camera.

Neuro-Cognitive Locomotion with Dynamic Attention on Topological Structure

This paper discusses a mechanism for integrating locomotion with cognition in robots. We demonstrate an attentional ability model that can dynamically change the focus of its perceptual area by integrating attention and perception to generate behavior. The proposed model considers both internal sensory information and also external sensory information. We also propose affordance detection that identifies different actions depending on the robot’s immediate possibilities. Attention is represented in a topological structure generated by a growing neural gas that uses 3D point-cloud data. When the robot faces an obstacle, the topological map density increases in the suspected obstacle area. From here, affordance information is processed directly into the behavior pattern generator, which comprises interconnections between motor and internal sensory neurons. The attention model increases the density associated with the suspected obstacle to produce a detailed representation of the obstacle. Then, the robot processes the cognitive information to enact a short-term adaptation to its locomotion by changing its swing pattern or movement plan. To test the effectiveness of the proposed model, it is implemented in a computer simulation and also in a medium-sized, four-legged robot. The experiments validate the advantages in three categories: (1) Development of attention model using topological structure, (2) Integration between attention and affordance in moving behavior, (3) Integration of exteroceptive sensory information to lower-level control of locomotion generator.

The Design of a New 3D Print-in-place Soft Four-Legged Robots with Artificial Intelligence

Soft and flexible robots are designed to change their flexibility over a wide range to perform tasks adequately in real-world applications. Current soft robots require cast moulding, high assembly effort and large actuators. Soft origami structures exhibit high levels of compliance. In this paper, we designed a new 3D print-in-place soft four-legged robot (3DSOLR). Our soft legged robot is an endurance application adapted from the soft origami zigzag gripper. This novel and innovative design are inspired by the rigid joint Theo Jansen legged robot with highly adaptive 3D print-in-place soft origami legs capable of fluid motion and even surviving drop tests. The robot mechanism consists of four soft origami flexible legs driven by two DC motors. The 3DSOLR is lightweight and semi-autonomous using two Hall effect sensors and a wireless Bluetooth module. Being 3D print-in-place using Thermoplastic polyurethane also increases its durability while having flexibility, simplicity and safety. The robot also has a gripper inspired by the mandible of male European stag beetle (Lucanus cervus). These features make this robot suitable to be used in social robotics and rescue robotics applications. The transmitter program is implemented in Bluetooth serial communication using MIT App Inventor 2 smartphone apps and a microcontroller Arduino ATMEL is used as the main controller and code in Arduino IDE. It has artificial intelligence (AI) capability with ESP32 CAM onboard which has an object classification accuracy of 95.5% using custom Edge Impulse neural network MobileNetV1 96 x 96. This AI capability enhanced the robot’s capability in object classification for grasping.

Leader–follower formation control of four-legged robots with discrete-valued inputs

This paper addresses a leader–follower formation control problem for four-legged robots under discrete-valued input constraints. Four-legged robots are more suitable for rough terrain missions than wheeled robots because the tip positions of their legs can be changed depending on the terrain. However, it is difficult to control these robots through continuous-valued inputs because they are steered by switching between specific movements (i.e., discrete-valued signals). Motivated by this fact, we have proposed controllers that achieve fixed formations of four-legged robots using discrete-valued inputs, but moving formations, which are necessary for some applications, have not been considered yet. Herein, we present a solution to the above leader–follower formation control problem based on the combination of PD-like formation controllers and dynamic quantization. We further introduce a performance index to evaluate the difference between two systems whose inputs are quantized and unquantized and analyze the index for the feedback system with the presented controllers. As a result, an upper bound of the performance index is derived as a function of the system parameters. This is useful to evaluate the impact of the quantization on the behavior of the feedback system and to provide a theoretical guarantee of the stability of the system.

Time Efficiency Improvement in Quadruped Walking with Supervised Training Joint Model

To generate stable walking of a quadruped, the complexity of the configuration of the robot involves a significant amount of optimization that decreases to its time efficiency. To address this issue, a machine learning method was used to build a simplified control policy using joint models for the supervised training of quadruped robots. This study considered 12 joints for a four-legged robot, and each joint value was determined based on the conventional method of walking simulation and prepossessed, equaling 2508 sets of data. For data training, the multilayer perceptron model was used, and the optimized number of epochs used to train the model was 5000. The trained models were implemented in robot walking simulations, and they improved performance with an average distance error of 0.0719 m and a computational time as low as 91.98 s.

ROMERIN: A new concept of a modular autonomous climbing robot

Climbing robots play an essential role in performing inspection work in civil infrastructures. These tasks require autonomous robots with competitive costs and the ability to adapt to different types of environments. This article presents ROMERIN, a new concept of a modular legged climbing robot where each leg is an autonomous robotic module in terms of processing capacity, control, and energy. The legs are equipped with suction cups that allow the robot to adhere to different types of surfaces. The proposed design allows the creation of climbing robots with a different number of legs to perform specific inspection tasks. Although each of the legs acts as an independent robot, they have the ability to share information and energy. The proposed control concept enables the development of climbing robots with the ability to adapt to different types of inspection tasks and with resilience characteristics. This article includes a description of the mechatronic design, the kinematics of the seven degree-of-freedom robotic legs, including the adhesion system, and the architecture of the control and simulation system. Finally, we present experimental results to test the modularity concept, mechanical design, and electronics using a four-legged robot configuration. We analyze the performance of the gripping system in different situations on four different surfaces and the behavior of the control architecture for two different robot body trajectories.

A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots

A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots

Design, analysis and method of foot trajectory generation for quadruped robots in swing-stance phase

PurposeLegged walking robots have numerous advantages over the wheel or tracked robots due to their strong operational ability and exposure to the complex environment. This paper aims to present details about the mechanical formation and a new conceptual elliptical trajectory generation discussed throughout the paper of the quadruped robot.Design/methodology/approachInitially, a realistic CAD model of the four-legged robot is developed in Solidwork-2019. The proposed model’s forward and inverse kinematics equations are deduced using Denavit–Hartenberg parameters. Based on geometry and kinematics, manipulability and obstacle avoidance are investigated. A method of galloping trajectory is proposed for aiming the increase of upright direction impulse, which is produced by ground reaction force at each step frequency. Furthermore, the locomotion equation of the ellipse trajectory is derived by setting transition angle polynomial of free-fall phase, stance phase and swing phase and the constraints.FindingsFinally, a successive simulation on a 2D sagittal plane is performed to check and verify the usefulness of the proposed trajectory. Before the development of the full quadruped, a single prototype leg is generated for experimental verification of the dynamic simulations.Originality/valueThe proposed trajectory is novel in that it uses force tracking control, which is intended to improve the quadruped robot’s robustness and stability.

Formation Control of Four-Legged Robots Using Discrete-Valued Inputs

This letter addresses a formation control problem of four-legged robots with discrete-valued inputs. Four-legged robots have the potential for completing tasks on non-flat terrains, while their position control is achieved by switching between specific movements, i.e., discrete-valued signals. This implies that existing results assuming the use of continuous-valued inputs are not available. We present a solution to this control problem by combining conventional formation controllers with dynamic quantization. The resulting feedback system is then analyzed based on a performance index describing the difference between the systems with and without quantization. As a result, we can estimate the effects of the quantization on the behavior of the system and guarantee its stability.

Bioinspired and Energy-Efficient Convex Model Predictive Control for a Quadruped Robot

Animal running has been studied for a long time, but until now robots cannot repeat the same movements with energy efficiency close to animals. There are many controllers for controlling the movement of four-legged robots. One of the most popular is the convex MPC. This paper presents a bioinspirational approach to increasing the energy efficiency of the state-of-the-art convex MPC controller. This approach is to set a reference trajectory for the convex MPC in the form of an SLIP model, which describes the movements of animals when running. Adding an SLIP trajectory increases the energy efficiency of the Pronk gait by 15 percent over a range of speed from 0.75 m/s to 1.75 m/s.

Development of Four-legged Robot

Development of Four-legged Robot

Gait Control Applications on Four Legged Robot

A common problem faced by four-legged robots is when they encounter obstacles that have structures that force the robot to lift its legs higher than the robot's ability limit and can disrupt the robot's balance, so it is necessary to add a gait control method. This research has resulted in controlling the movement of a four-legged robot using the wave gait control method to pass obstacles in the form of stairs and uneven floors, where the movement of the robot by lifting one leg at a time makes the balance of the robot very awake. So that the robot can make decisions if the robot encounters obstacles in the form of stairs and uneven floors, the role of the ultrasonic sensor is very decisive in reading the environment. The way for the robot to climb stairs and not fall backwards is to slide the robot's hind legs back so that each robotic footstep will form a triangle. When the robot detects the presence of a stairs, the robot will lift its legs by 50o, while when it detects an uneven floor, the robot's legs will lift its legs by 40o. Based on the results of research conducted with 4 experiments, namely the success of the robot in passing through the trajectory without obstacles by 100%, the success of the robot in crossing the uneven floor by 60%, the success of the robot in passing the stairs by 50%, while the success of the robot in passing the stairs and the uneven floor by 70%.

Robots with Tails

Abstract Until recently, most four-legged robots have lacked a feature that is found again and again in nature—a tail. Studies of animal locomotion and robots in the laboratory indicate that leaving out tails has been a design drawback. In fact, research conducted by our lab at Virginia Tech has shown that an articulated robotic tail can effectively maneuver and stabilize a quadruped both for static and dynamic locomotion.