Walking on different terrains leads to different biomechanics, which motivates the development of exoskeletons for assisting on walking according to the type of a terrain. The design of a lightweight soft exoskeleton that simultaneously assists multiple joints in the lower limb is presented in this paper. It is used to assist both hip and knee joints in a single system, the assistance force is directly applied to the hip joint flexion and the knee joint extension, while indirectly to the hip extension also. Based on the biological torque of human walking at three different slopes, a novel strategy is developed to improve the performance of assistance. A parameter optimal iterative learning control (POILC) method is introduced to reduce the error generated due to the difference between the wearing position and the biological features of the different wearers. In order to obtain the metabolic rate, three subjects walked on a treadmill, for 10 min on each terrain, at a speed of 4 km/h under both conditions of wearing and not wearing the soft exoskeleton. Results showed that the metabolic rate was decreased with the increasing slope of the terrain. The reductions in the net metabolic rate in the experiments on the downhill, flat ground, and uphill were, respectively, $9.86\%$, $12.48\%$, and $22.08\%$ compared to the condition of not wearing the soft exoskeleton, where their corresponding absolute values were $0.28$ W/kg, $0.72$ W/kg, and $1.60$ W/kg.

Safety is one of the critical challenges in the autonomous driving task. Recent works address the safety by implementing a safe reinforcement learning (safe RL) mechanism. However, most approaches make conservative decisions without knowing the confidence of the actions, which ultimately causes traffic congestion and low travel efficiency. This paper proposes an uncertainty-augmented Lagrangian safe reinforcement algorithm (Lag-U) to improve exploration and safety performance for autonomous driving. First, epistemic uncertainty is introduced into safe RL by using deep ensemble. We use the estimated epistemic uncertainty to encourage exploration and to learn a risk-sensitive policy by adaptively modifying safety constraints. Second, we facilitate an intervention assurance to choose safer actions based on the quantified epistemic uncertainty during deployment. Experimental results prove that the proposed method outperforms other safe RL baselines. The trained vehicle can make a decent trade-off between high efficiency and avoiding risks, thus preventing ultra-conservative policy.

Purpose: Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements. Design/methodology/approach: A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots. Findings: The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system. Originality/value: Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

This article addresses the consensus problem of a class of unknown nonlinear multi-agent systems (MASs) under directed graphs via a novel model-free deep reinforcement learning (DRL) based fully distributed event-triggered control (ETC) method. First, the DRL-based feedback linearization approach is developed to learn an approximated linearized control protocol in a model-free manner. Then, a novel adaptive event-triggered mechanism (ETM) is proposed to save more communication resources and reduce the computational burden among agents, and the Zeno behavior is ruled out strictly. The control protocol proposed in this paper does not involve global information, thus it can be implemented in a fully distributed manner. Furthermore, a new Lyapunov function is constructed using a graph-based diagonal matrix to achieve the consensus of MASs under directed graphs. Generally, distinct from the existing results, the proposed model-free DRL-based fully distributed ETC protocol has the following features: 1) only using the intermittent local information, 2) not requiring the model information and global graph information, and 3) applicable to the more general directed graph. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.