With the emerging of wearable robots, the safety and effectiveness of human-robot physical interaction have attracted extensive attention. Recent studies suggest that online measurement of the interaction force between the robot and the human body is essential to the aspects above in wearable exoskeletons. However, a large proportion of existing wearable exoskeletons monitor and sense the delivered force and torque through an indirect-measure method, in which the torque is estimated by the motor current. Direct force/torque measuring through low-cost and compact wearable sensors remains an open problem. This paper presents a compact soft sensor system for wearable gait assistance exoskeletons. The contact force is converted into a voltage signal by measuring the air pressure within a soft pneumatic chamber. The developed soft force sensor system was implemented on a robotic hip exoskeleton, and the real-time interaction force between the human thigh and the exoskeleton was measured through two differential soft chambers. The delivered torque of the hip exoskeleton was calculated based on a characterization model. Experimental results suggested that the sensor system achieved direct force measurement with an error of 10.3 ± 6.58%, and torque monitoring for a hip exoskeleton which provided an understanding for the importance of direct force/torque measurement for assistive performance. Compared with traditional rigid force sensors, the proposed system has several merits, as it is compact, low-cost, and has good adaptability to the human body due to the soft structure.

To improve the control performance of a permanent magnet synchronous motor (PMSM) under external disturbances, an improved active disturbance rejection control (IADRC) algorithm is proposed. Since the nonlinear function in the conventional ADRC algorithm is not smooth enough at the breakpoints, which directly affects the control performance, an innovative nonlinear function is proposed to effectively improve the convergence and stability. On this basis, the proposed IADRC is constructed, and comparative simulation results with ADRC and other IADRC show that faster response speed, higher accuracy and stronger robustness are obtained.

Semi-structured orchard environment has the characteristics of high complexity and strong uncertainty, which brings many challenges to the localization and navigation of agricultural automation equipment with limited sensors. This paper introduces a tree trunk localization system in a semi-structured orchard using camera/ultrasonic sensors fusion. In our method, the location of each tree trunk, which is relative to the robot, is obtained through the trunk tracking camera/ultrasonic detection system on the robot. And the location and direction of the robot are obtained through wheel odometer and IMU, respectively, rather than the traditional high-precision RTK-GPS. In order to reduce the error caused by odometer and IMU, several control points are introduced to revise the tree trunks’ location, of which positions are determinate. Experiments suggest that in a simulated environment and a real orchard, which both have a row-column spacing of $4.3m\times 3.4m$, the tree trunk localization error of our approach is within 0.3m.