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In this letter, we present an underactuated soft exoskeleton which can sequentially provide knee extension and ankle plantarflexion assistance for each leg with only one motor. The aim of this work is to assist the motions that have chronological moments at lower-limb joints and minimize the mass carried by the wearer. The underactuated soft exoskeleton consists of a novel actuation mechanism: Continuum-Cable System (CCS), which allows the actuators to transmit assistive torques to knee joint when the knee is flexed and transmit assistive torques to ankle joint when the knee is fully extended. Design concepts and component descriptions were elaborated for this underactuated soft exoskeleton, including kinematics, actuation, etc. The performance of the underactuated soft exoskeleton was evaluated by a series of experiments. With effective assistance from the proposed exoskeleton, the power of the knee was reduced by 6.40%, compared to without exoskeleton condition. Meanwhile, the moment and power of the ankle also demonstrated significant reduction when comparing the exoskeleton powered condition to the without exoskeleton condition (reduced by 11.61% and 45.44%, respectively). This underactuated soft exoskeleton represents a feasible solution to apply sequential assistance to the knee and ankle, and the experimental results validated its effectiveness in assisting the motions which have chronological moments at each lower-limb joint.

During COVID-19, with a heavy sprayer filled with disinfectant, the risk of infection for epidemic prevention personnel has been increased by long-term environmental disinfection. In order to reduce the burden and save energy of human, this letter proposed a Wheel-Legged Robotic Limb (WRL) for the carriers. The mass of WRL is only 1.77 kg. The WRL has one rigid robotic limb located below the sprayer, which can provide active supporting force for the sprayer. The WRL adopts force closed-loop control method to ensure the system provide an expected supporting force. The system performance was evaluated including standing and walking at 5 km/h, under three experimental conditions included: 1) with a sprayer, 19.41 kg (SPRAYER), 2) with the powered WRL, 22.18 kg (WRL_ON), and 3) with the unpowered WRL, 22.18 kg (WRL_OFF). When the supporting force is set as 80 N, the experimental results show that the WRL_ON condition has reduced the vertical load force on the human, the vertical ground reaction force of human feet, and the metabolic power by 41.28%, 8.03%, and 17.46% during standing, and also reduced by 32.29%, 8.08% and 18.92% during walking, compared to SPRAYERcondition, respectively.

Predicting the locomotion intent of humans is important for controlling assistive robots. Previous studies have investigated assistive walking on structured terrains, but only a few studies have considered rough terrains. Human intent on rough terrains is more difficult to predict because there is a transition at every step. To predict the foot placements of humans on rough terrains, the present paper fuses sequential 3D gaze and the environmental context. The 3D gaze is assumed to be the intersection point of the line of sight as measured by an eye-tracker and the environmental point cloud as measured by an RGBD camera. The sequential 3D gaze and the environmental context are fused based on an RGBD SLAM algorithm. Then the segmented terrain that is closest to the center of sequential 3D gaze is regarded as the most possible foothold area at the next step. Six able-bodied subjects are invited to walk randomly on rough terrains. Their foot placements are labeled and compared with the predicted foot placements. Experimental results show that the proposed method can predict the foot placements of all subjects 0.5 step ahead. With environmental context and user-dependent time window, the distance error of predicting the foot placements can decrease to 0.086 m. Hence, gaze, environmental context, and time window are all important in predicting the human intent when navigating rough terrains.

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect the estimation results. In this paper, a more robust gait phase estimation method is proposed using a unified form of piecewise monotonic gait phase-thigh angle models for various locomotion modes. The gait phase is estimated from only the thigh angle, which is a stable variable and avoids phase drifting. A Kalman filter-based smoother is designed to further suppress the mutations of the estimated gait phase. Based on the proposed gait phase estimation method, a gait phase-based joint angle tracking controller is designed for a transfemoral prosthesis. The proposed gait estimation method, the gait phase smoother, and the controller are evaluated through offline analysis on walking data in various locomotion modes. And the real-time performance of the gait phase-based controller is validated in an experiment on the transfemoral prosthesis.

An increasing number of robotic systems involving lots of robotic individuals are used to serve human, such as intelligent terminal, intelligent storage, intelligence factories, etc. It is a trend of robotics technology that robotics system will become huger with more individuals. In these systems, they form the robotic societies and need establish some computing rules and mechanisms to ensure the operation like all biological social systems. In this paper, a novel system architecture for swarm robotic system, including three layers: human–computer interaction layer, planning layer and execution layer, is put forward, which is effective for task-oriented swarm robotic system. Then, a hierarchical organizational model for the system is presented, which is used to establish management relationship between different layers and individuals. Because task-oriented characteristic is required, this paper elaborates task description knowledge to explain the relationship between tasks for task decomposition and task logic. In addition, a method of behavior generation based on proposition/transition Petri networks is designed, which would effectively assist the system to construct combined behavior using simple individual behavior to solve a variety of tasks. At last, Illustration is shown to prove effectiveness and an implementation of the method based on SociBuilder system is introduced.