Terrain classification is crucial for assessing terrain traversability and supporting locomotion control of legged robots. By integrating multi-source sensor information, including exteroceptive sensors and proprioceptive sensors, legged robots can acquire terrain geometric features and surface cover types. However, single-sensor approaches exhibit inherent limitations, where exteroceptive sensors are susceptible to environmental interference while proprioceptive sensors struggle to identify surface cover types. To address these challenges, this paper proposes a robust terrain classification framework that overcomes the limitations of single-modal perception through fusion of exteroceptive and proprioceptive sensors. The framework comprises a Golden Sine Optimization Algorithm-based random forest model using proprioceptive sensors to determine optimal hyperparameter combinations based on classification requirements, and a YOLOv11 network integrated with intersection over union object tracking algorithm to achieve stable image extraction during robot movement. Final terrain classification is accomplished through Kalman filter-based decision fusion. Experimental validation demonstrated classification accuracies of 94.4% for the proprioceptive module and 94.2% for the visual module in offline testing. In online fusion testing, the system achieved 95.9% overall classification accuracy, confirming the effectiveness and engineering practicality of the proposed method.

Humanoid robots are vital tools for substituting humans in various operational scenarios. A sufficiently large stationary reachability is a key factor in ensuring their operational capability. To address this challenge, this paper proposes a whole-body reachability enhancing approach for humanoid robots based on gradient optimization, referred to as Gradient-based Hierarchical Optimization Whole-Body Control (GHO-WBC). The goal of the proposed approach is to extend the end-effector reachability of the humanoid robot while maintaining its stationary state. The proposed approach first derives the gradient of the robot’s whole-body center of mass (CoM) position, ensuring stationary stability across extreme reachable ranges. Next, the gradient of the key joint segment singularity is derived to achieve the stability of the humanoid robot’s end effector at extreme operational distances. Finally, a multi-level optimization approach is employed to compute a feasible solution for the whole-body joint kinematics, and experimental validation is conducted on the humanoid robot. Compared to the conventional whole-body control optimization approach, the present approach improves the reachable range by more than 89%.

Human-Computer Interaction (HRI) is crucial in robotics. This article introduces a new method for human-machine interaction on robotic arms using a drag teaching approach. By analyzing the kinematics and dynamics of the Xarm7 robotic arm, the method reduces positioning difficulties. The F/T sensor on the arm detects force and torque. Direct human guidance is matched with robot response in real-time to control the arm's position and direction. The research also records and reproduces motion trajectories using cubic interpolation for precision. A mechanical claw on the arm detects and compares sensor data to position and grasp objects. Experimental results show similar shapes between the dragging and reproduced trajectories, with reduced motion errors.

Compared with torque-control techniques, a position-controlled quadruped robot is lower cost, easier to build, and more direct to drive. However, the stiff actuation of position-controlled actuators makes it difficult for the quadruped to achieve dynamically stable locomotion. This paper presents an implementation of joint velocity programming technique to regulate the body’s moving speed and orientation for a position-controlled quadruped robot that performs trotting or pacing locomotion. The robot model is mapped to a new coordinate space in order to decouple the control of its body. In one plane of the new coordinate space, the robot is simplified to an inverted pendulum model to generate attitude and velocity tracking actions. In the other planes, body regulating problems are formulated in velocity forms and solved by designing support leg motions. The controllers in these planes are integrated to produce joint velocities that enable robust trotting and pacing locomotion at a variety of speeds and directions, despite lacking force control or feedback techniques. Physical test results as well as simulating results demonstrate control of the quadruped robot SmarQ to perform omni-directional locomotion, impact recovery, and adaptability to uneven terrains.

To access rough terrain and enhance the mobility in sandy terrain, a configurable paddle-wheel robot was pro-posed. This report addresses the paddle terradynamics, and the experimental verification of the locomotion performance of the robot over dry sandy terrain. To study the interactive forces between the paddle and the media, a terradynamic model is built and verified through experiments. To explore the locomotion performance, an indoor platform that allows the paddle-wheel module to move freely in both horizontal and vertical directions is created. Forward locomotion speed, height variant, and specific resistance are evaluated with different con-figurations. The protruding paddles have successfully reduced the slippage so as to increase the locomotion efficiency in sandy terrain. The performance of the whole robot has also been verified in outdoor sandy terrain.

A floating base system is inevitably to contact the environment while it is moving. This paper explores the contact force estimation and regulation algorithm for a position-controlled floating base system without joint torque information. First, the joint space dynamic model of the system is presented and transformed into the contact space. Then, the inverse dynamics method is employed to estimate the contact forces. After that, a proportional-integral (PI) regulator is designed to drive the contact forces to track the desired values. Finally, the feasibility of this algorithm is demonstrated on a simulated bipedal platform.

Nonprehensile manipulation produces underconstraint motions that are sensitive to environmental dynamics. Legged locomotion constitutes a floating-based movement, whose dynamic is underactuated with respect to the inertial frame. When these two tasks are combined, system motion planning and control are complex due to their inherent underactuated features. This article presents a motion planning framework for a legged robot that uses its limbs for nonprehensile manipulation, as well as locomoting motions. First, issues related to the description of the robot-object-environment system and the task are presented. The velocity constraint that prevents separation and the force constraint that restricts interactive forces are then integrated into the system dynamic model to produce bounds on the system acceleration as a function of the system state. Then, we solve the motion planning problem by reducing the system dimensions in operational space and programming feasible trajectories within the phase plane. This approach is employed to control the quadruped robot TITAN-VIII to manipulate objects and locomote itself using Drive Mode, Inchworm Mode, Scoot Mode, and Throw Mode. Experimental results obtained through simulations and physical tests are reported to demonstrate the effectiveness of our approach.

This paper presents the dynamic modelling and motion planning method for a quadruped robot that uses its legs for nonprehensile manipulation as well as locomotion. Three different working modes named Drive Mode, Inchworm Mode and Scoot Mode are proposed to enable the robot to move forward together with the object. We firstly introduce a universal model for these modes and deduce its dynamic equation. Then the contact force constraints are combined and mapped to the system state variables. Based on the acquired state acceleration constraints, the motion planning problem can be solved by designing system state paths in the phase space. After that, we described the mathematical problems within the three working modes and generate the robot motions accordingly. Finally, experimental results obtained through simulations and physical tests are reported to demonstrate the effectiveness of our method.

This paper explores the control strategy for a multi-limb robot nonprehensilely pushing an object to slide on the floor. The robot's limb distals perform point contacts with the object and the floor. The contact velocity constraint and force constraint are proposed to prevent separation and restrict the system forces. Then the constraints are combined with the system dynamic models to obtain bounds on the system states. We solve the motion planning problem by selecting a feasible path in the reduced-dimensional space and generating the system trajectory along the selected path. An example is provided to illustrate the application of our technique on the physical platform.

A novel gait planning and control framework was developed for quadruped locomotion of a robot. It modeled the quadruped robot as two bipeds carrying the body from the front and rear ends. We first mapped the relationship between the joint torques of support legs and the torso forces of the bipedal sub-robots. Then the equations describing the relationship between the quadruped body forces and the bipedal torso forces under various operating modes of the robot were deduced and solved. Virtual forces were generated on the quadruped body to manipulate its velocity and orientation. Then these virtual forces were distributed to the front and hind sub-robots to generate support leg torques. The state machines and gait generators for the two bipedal sub-robots were designed individually, resulting in the decoupling of the gait parameters in the front legs and hind legs. The effectiveness of the controller was validated through dynamic simulations.