Multi-legged robots offer enhanced stability to navigate complex terrains with their multiple legs interacting with the environment. However, how to effectively coordinate the multiple legs in a larger action exploration space to generate natural and robust movements is a key issue. In this paper, we introduce a motion prior-based approach, successfully applying deep reinforcement learning algorithms to a real hexapod robot. We generate a dataset of optimized motion priors, and train an adversarial discriminator based on the priors to guide the hexapod robot to learn natural gaits. The learned policy is then successfully transferred to a real hexapod robot, and demonstrate natural gait patterns and remarkable robustness without visual information in complex terrains. This is the first time that a reinforcement learning controller has been used to achieve complex terrain walking on a real hexapod robot.

Guiding the visually impaired in complex environments requires real-time two-way interaction and safety assurance. We propose a Force-Compliance Model Predictive Control (FC-MPC) and Robot-User Control Barrier Functions (CBFs) for force-compliant navigation and obstacle avoidance in Hexapod guide robots. FC-MPC enables two-way interaction by estimating user-applied forces and moments using the robot’s dynamic model and the recursive least squares (RLS) method, and then adjusting the robot’s movements accordingly, while Robot-User CBFs ensure the safety of both the user and the robot by handling static and dynamic obstacles, and employ weighted slack variables to overcome feasibility issues in complex dynamic environments. We also adopt an Eight-Way Connected DBSCAN method for obstacle clustering, reducing computational complexity from O($n^{2}$) to approximately O(n), enabling real-time local perception on resource-limited on-board robot computers. Obstacles are modeled using Minimum Bounding Ellipses (MBEs), and their trajectories are predicted through Kalman filtering. Implemented on the HexGuide robot, the system seamlessly integrates force compliance, autonomous navigation, and obstacle avoidance. Experimental results demonstrate the system’s ability to adapt to user force commands while guaranteeing user and robot safety simultaneously during navigation in complex environments. Note to Practitioners—Guiding visually impaired individuals in complex environments remains a significant challenge, as traditional methods like guide dogs are limited by high costs and scalability issues. To address this, we propose a Hexapod guide robot framework that integrates Force-Compliance Model Predictive Control (FC-MPC) and Robot-User Control Barrier Functions (CBFs) to ensure real-time two-way interaction and dual safety for both the user and the robot. The framework supports diverse interaction modes by allowing parameter adjustments to combine functionalities such as force compliance, global autonomous navigation, and obstacle avoidance. An efficient clustering algorithm is adopted in the local environment perception process, which enables real-time deployment on resource-constrained devices of guide robot. While effective in experiments, future work will focus on map-less navigation to improve performance in previous unseen environments.

Wheel-legged planetary rovers possess superb locomotion capabilities. This article combines an offline predefined motion planning library with online path planning, integrating energy consumption and probabilistic aspects of the robotic system. The primary focus is on addressing the planning challenges in dense environments, where the distance between any adjacent obstacles is smaller than the width of the prototype. Therefore, it is necessary to consider the interaction between the prototype and the environment. First, the generalized function set theory and the configuration topology theory are utilized to mathematically describe the motions of multilimbed systems. Based on the representation, an offline planning library is established. Second, the Markov-decision-process-based path planning method is extended by incorporating the platform's geometry and locomotion capabilities. The concept of “limb-travel relevant nodes” is introduced. To address the numerous iteration problems, the informed value iteration algorithm is proposed. Third, a multilayered map is evaluated to further enhance computational efficiency. Finally, the proposed algorithm is implemented on the terrain adaptive wheel-legged rover. Experimental results demonstrate that the proposed algorithm is capable of finding the optimal path with high computational efficiency, and it exhibits excellent adaptability on nonuniform maps.

Social robots require the ability to understand human emotions and provide affective and behavioral responses during human-robot interactions. However, current social robots lack empathy capabilities. In this work, we propose a novel Multi-modal Hierarchical Empathetic (MHE) framework for generating empathetic responses for social robots. MHE is composed of amulti-modal fusion and emotion recognitionmodule, anempathetic dialogue generationmodule, and anexpression generationmodule. By fusing the sensor signals of different modalities, the robot can recognize human emotions and generate affective responses. Multiple experiments are conducted on a real robot, Pepper, to evaluate the proposed framework. The experiments are conducted to discriminate between MHE-generated text and human responses in complete ignorance, and most experimenters agree that MHE can effectively generate human-like and empathetic responses. To better evaluate the similarity between human-robot and human-human interactions, a period eye movement map (PEM) captured by an eye tracker is proposed. The experimental results demonstrate the improvement in the MHE in human-robot interactions by comparing different PEMs.

Staircase is a typical obstacle for the legged robot to overcome in buildings. This paper studies the stair climbing capability-based dimensional synthesis for a hexapod legged robot, i.e., exploring how to determine the leg length and the longitudinal body length concerning the target staircase in the mechanical design stage. In climbing a staircase, leg-staircase interference is one of the predominant issues. The three possible interference cases are illustrated in detail with a 2-DOF (degree of freedom) leg mechanism and the staircase size, based on the predefined tripod gait sequence. The mathematical relationships between the leg length, longitudinal body length, and the target staircase size are derived. The leg length and the body length are finally determined with the target staircase size. The virtual simulations and prototype experiments verify the effectiveness of the dimensional synthesis for the hexapod robot.

The autonomous robots consisting of an immovable lander and a rover are widely deployed to explore extraterrestrial planets. However, these robots have two main limitations: (1) the separate design for lander and rover respectively results in heavy mass and big volume of the whole system, which increases the launching cost sharply; (2) the rover’s detection area has to be restricted to the vicinity of the immovable lander. To overcome these problems, we designed a novel six-legged mobile repetitive lander called "HexaMRL", which integrates the functions of both lander and rover, including folding, deploying, repetitive soft-landing, and walking. A hybrid compliant mechanism taking advantages of both active and passive compliances was adopted on its leg. An integrated drive unit (IDU) was utilized to imitate the dynamics of a spring and a damper to absorb the landing impact energy, while the structure remains intact. Moreover, a control method based on state machine for soft-landing on the Moon was proposed. HexaMRL achieved repetitive soft-landing on a 5-DoF lunar gravity testing platform (5-DoF-LGTP) with a vertical landing velocity of 1.9 m/s and a payload of 140 kg. The drive torque safety margin is improved by 23.4%p based on the hybrid compliant leg comparing with the standalone active compliant leg.

A major problem for a mobile robot aiming to work with people is how to move safely in a crowded environment where the robot-human contact is inevitable. However, most existing solutions focus on finding a collision-free path. Moreover, few of them considers the legged robots, which are believed to find wide applications in human daily life due to their superior mobility in complex artificial environments. In this paper, we propose a novel tactile-based gait planning method for a hexapod robot to pass through crowds in a safe and stable manner. Firstly, we developed a body trajectory generator based on a virtual dynamic model with actual contact force feedback. Then, we developed a leg trajectory planner by taking into account the ZMP stability criterion. In such a way, the contact force can be actively controlled and the stable tripod walking can be realized. We verify our method in simulations and then implement it on the real robot, HexbotIII, for experiments. The experimental results show that with our method the robot can effectively bypass moving humans while regulating the contact force in a safe range.

Summary form only given. The design and control of quadruped robots has become a fascinating research field because they have better mobility on unstructured terrains. Until now, many kinds of quadruped robots were developed, such as JROB-1 [1], BISAM [2], BigDog [3], LittleDog [4], HyQ [5] and Cheetah cub [6]. They have shown significant walking performance. However, most of them use serial mechanism legs and have animal like structure: the thigh and the crus. To swing the crus in swing phase and support the body's weight in stance phase, a linear actuator is attached on the thigh [2, 3, 5, 6], or instead, a rotational actuator is installed on the knee joint [1, 4]. To make the robot more useful in the wild environment, e.g., the detection or manipulation tasks, the payload capability is very important. To carry the sensors or tools, heavy load legged robot is very necessary. Thus the knee actuator should be lightweight, powerful and easy to maintain. However, this can be very costly and hard to satisfy at the same time.

Walking on irregular terrain is usually a common task for a quadruped robot. It is however difficult to control the robot in this situation as undesirable impulse force by collision between the foot of robot and obstacles makes the robot unstable. This paper presents a Posture Feedback Compensation Controller (PFCC) for a quadruped robot with high payload walking on irregular terrain. In order to make the robot walk stably and fast on irregular terrain, we choose trotting gait for walking. The foot trajectory is scheduled based on the Bezier curve method in order to improve the stability of quadruped robot. Simulations of walking on irregular terrain have been performed. The results have verified that the proposed methods have better stability and higher speed for walking on the irregular terrain.

This research is concerned with the design and analysis of a parallel kinematic manipulator (PKM) with three degrees of freedom (DOF). The proposed PKM combining the spatial rotational and translational degrees of freedom has varied advantages and good potential applications of materials handling. First, the static balancing of the parallel manipulator is investigated. The definition and methodology of static balancing are introduced. Two methods including adjusting kinematic parameters and counterweights are applied to the structure and the counterweights method leads to static balancing of the PKM. The conditions of static balancing are given. Then the dynamic model of the proposed PKM is deduced. It describes the relationship between the driving forces and the motion of the end-effector platform. Two approaches, the Newton-Euler and the Lagrange methods, are compared and the later one is selected to build the dynamic model of the 3-DOF tripod mechanism.

This paper proposes a 6-DOF (Degree of Freedom) heavy-load parallel manipulator with a redundant actuation and fault-tolerant actuator (RFTA). The novel RFTA model of the proposed manipulator is developed and its working principle is described. In order to achieve the given motion, the mathematic models of the proposed manipulator with the RFTA are derived. As a prototype of an earthquake simulator, two experiments are performed. The experimental results demonstrate that the RFTA is able to supply the required double driving force and appropriate used as an actuator of a low frequency earthquake simulator. The results of the fault-tolerant experiment show the earthquake simulator with the RFTA is capable of tolerating some local faults. The proposed parallel manipulator can also be applied under other heavy-load environments.

In this paper, a new fully adaptive feedforward decentralized controller is developed for 6 degree of freedom (6DOF) parallel robot. This method makes the position error and velocity error converge to zero asymptotically. Theoretical analysis and simulation results are presented to illustrate the proposed approach. The controller parameter tuning method is also proposed.

Kinematics decoupling characteristics (KDCs) for parallel manipulators simplify the kinematics models, and make them easier in calibration and control. This paper focuses on KDCs for parallel manipulators which are described in detail. The relationship between input and output of a system with KDCs is discussed and the characteristics of its transfer matrix are educed. Then, the kinematics of two parallel manipulators, a 6-PSS parallel micro-manipulator with perpendicular structures (PMMWPS) and a 6-PP+S parallel manipulator with perpendicular structures (PMWPS), is analyzed. Also, the kinematics models are obtained. In the course of the analysis, conditional decoupling is defined. The results show that the two proposed PMWPSs have KDCs. The KDCs for PMWPSs would offer a new idea in the area of parallel manipulators