Terrain classification is crucial for robotic navigation especially in unknown environment. Existing terrain classification methods usually have high requirements for environment conditions and robot motions, making them challenging to apply to real-world scenarios. In this paper, we develop a novel terrain classification system with the planar electrical capacitance tomography (ECT) sensor, which provides a non-contact, real-time, and cost-effective way for terrain classification. Specifically, we design a planar ECT sensor and integrate it at the bottom of a mobile robot. The proposed system leverages the collected capacitance measurements to reflect the inherent differences in dielectric permittivity across various terrain types. And a multilayer perception networks is used to fuse the collected capacitance and IMU measurements for classification. Additionally, a large scale ECT dataset including 10 different types of terrains is collected with the proposed system. Extensive experiments are conducted demonstrating the effectiveness and robustness of the proposed system.

Tactile sensing plays a critical role in enabling robots to interact safely with target objects in dynamic and unstructured environments. While various tactile sensors based on different sensing principles or different sensitive materials have been proposed, the development of flexible large-area tactile sensors for robots is still challenging. In this paper, a novel highly sensitive piezoresistive sponge based on multi-walled carbon nanotubes (MWCNTs) and polyurethane (PU) sponge is fabricated for pressure sensing. The sensing behavior of the piezoresistive sponge was experimentally evaluated, showing high sensitivity and fast response. Based on the piezoresistive sponge, a flexible large-area tactile sensor is designed for distributed force detection with electrical resistance tomography technology. The sensing performance of the sensor is validated by touch location, sensitivity analysis, real-time touch discrimination, and touch modality recognition. The experimental results indicate that the sensor performs well in detecting the position and force of contact in a large area. The sensor’s performance shows promise in embodied tactile sensing and human–robot interaction.

It is critical to perceive physical contact for intelligent robots to safely interact in dynamic, unstructured environments. As physical contacts can occur at any location, a well-performing tactile sensing system should be able to deploy a large area on robotic surface. Some researchers have implemented large-area tactile sensors by using sensing arrays, but it is challenging to deploy many sensing elements. Electrical resistance tomography (ERT) has recently been introduced into tactile sensing to overcome some of the limitations with conventional tactile sensing arrays, and good results have been achieved for some robotic applications. However, a particular challenge is that spatial resolution is low. Although various attempts have been made to improve the performance of ERT-based tactile sensors, the intrinsic resolution issue remains unsolved. In this paper, we propose a novel adaptive optimal drive strategy for efficient ERT-based large-area tactile sensing for robotic applications, which can adaptively select the current injection and voltage measurement pattern for optimal tactile stimulus. In particular, regions of tactile contacts are preliminarily detected and localized by a base scanning pattern with only a few measurement data. According to this detected region, the adaptive strategy can select the optimal current injection and voltage measurement pattern to improve the sensing performance by maximizing the current density. To verify the effectiveness of the proposed strategy, the proposed method is comprehensively evaluated by simulation and experiments. The results revealed that the optimal strategy can effectively improve both spatial and temporal resolution.

Current trends in distributed computing facilitate data processing near the end user through Cloud-Fog-Edge architectures. Agricultural instrumentation applications can greatly benefit from edge computing signal processing, to provide detailed information about soil hydraulic conductivity towards improving irrigation planning and scheduling. A multimodal instrumentation scheme is constructed with a combination of field infiltrometer and wetting front detection to measure soil saturated hydraulic conductivity. Measurements were performed on a test lysimeter using sandy loam soil. The results indicate that the combined instrumentation can provide detailed information of the soil hydraulic conductivity which has important implications for irrigation scheduling.

Because more and more solid waste is generated, in particular, in cities, the management of solid waste disposal has become a global challenge. A solution is to find an effective way to sort solid waste materials and recycle them into reusable products. In this article, we propose to use a material recognition method with vision-guided tactile to form a robotic system for waste sorting. The vision guidance module integrates an object detector and an affordance network together. It allows the robot to not only detect the desired containers and packaging from an assortment of the waste but also obtain a configuration to grasp the target and actively collect its tactile data. By classifying the object with the tactile data, the robot can sort containers and packaging into their respective categories according to the type of material. Our experimental results demonstrate the effectiveness of the proposed robotic waste sorting system in sorting containers and packaging various types of materials. Note to Practitioners—The management of waste has become a great challenge in environmental protection in the world. We propose a robotic waste sorting system, which utilizes a vision-guided tactile sensing approach to find target waste and sort the waste according to materials. A visual module is used to find the waste of interest. A class-specific affordance map is generated to guide a robotic hand to actively grasp waste and collect the tactile data for material recognition. With the recorded tactile data, the robot can recognize the material of the target waste and sort the waste according to the type of material. The proposed system demonstrates good performance in waste sorting, and it can be easily implemented in practical scenarios. The target material can be generalized to a broader group of objects.

The smart city is an integrated environment that heavily relies on intelligent robots, which provides the basis for the warehouse automation. However, a warehouse is a typical unstructured environment, and robotic grasp and manipulation are extremely important for the package, transfer, search, and so on. Currently, the most usual method is to detect the picking or grasping points for some specific end-effector including suction cup, gripper, or robotic hand. The manipulation performance is, therefore, strongly influenced by the visual detector. To tackle this problem, the affordance map has recently been developed. It characterizes the operation possibilities afforded by the operation scene and has been used for several grasp tasks. Nevertheless, the conventional affordance method often fails in complicated environments due to the mistake calculation results. In this article, we develop a novel framework to integrate the interactive exploration with a composite robotic hand for robotic grasping in a complicated environment. The exploration strategy is obtained by a deep reinforcement learning procedure. The developed new composite hand, which integrates the suction cup and grippers, is used to test the merits of the proposed interactive perception method. Experimental results show the proposed method significantly increases the manipulation efficiency and may bring great economic and social and benefits for smart cities.