Aerial manipulator (AM) systems are significantly more effective than both conventional manipulators and flying robots, especially in disaster rescue settings, because they can perform human-like interaction tasks in flight. However, controlling the contact force of an AM is difficult due to the coupling between its flying platform and manipulator. This paper proposes a contact force control framework to address this problem. First, the UAV/AM position response under an external force is studied, and it is theoretically shown that a closed-loop UAV/AM behaves as a spring-mass-damper system. Second, a contact force controller is designed using an inverse-dynamics method. Third, an attitude feed-forward approach is employed to improve the force tracking performance. Then, the real-time contact position is introduced into the control system to achieve interaction with an actively moving environment. Finally, an innovative AM is developed and subjected to flight experiments, validating the proposed framework. An AM’s characteristics in interaction operations are summarized, and general conclusions are drawn. This study is novel in that 1) the contact force control is implemented without relying on force sensors, 2) the whole framework is applicable for controlling a constant/variable contact force with high closed-loop performance, and 3) it can also perform reliable interaction with a dynamic and unknown environment.Note to Practitioners—This study is motivated by the contact force control problem of an aerial manipulator (AM) during interaction operations. Previous approaches have explored the feasibility of controlling contact forces to some extent, but they lack universality and assume a static environment. Ensuring sufficient safety and providing reliable solutions in real-world applications remain challenging. This paper aims to investigate the position response of a closed-loop aircraft system under external forces, without disrupting its existing steady flight. The proposed method transforms contact force control into position control, eliminating the need for a force sensor. A series of aerial experiments were conducted to validate the effectiveness and applicability of the method in various scenarios. Our ongoing work will focus on migrating such an AM system from a laboratory scenario to the real world.

Spatial range query enjoys widespread application scenarios due to the ever-growing geo-positioning technology in recent years. Huge amounts of encrypted geo-location data are being outsourced to cloud servers to alleviate local storage and computational overheads without leaking sensitive information. However, most existing Privacy-preserving Spatial Range Query (PSRQ) cannot achieve high efficiency while satisfying strong security over large-scale encrypted spatial data. To strike a best possible balance between security and efficiency, we propose a novel efficient Privacy-preserving Spatial Range Query (eP-SRQ) scheme in dual-cloud architecture over large-scale dataset. Specifically, we propose an efficient PSRQ scheme by designing a novel index structure based on Geohash algorithm, Circular Shift and Coalesce Zero-Sum Garbled Bloom Filter (CSC-ZGBF) and Symmetric Homomorphic Encryption (SHE), which makes the computational complexity of query process independent of dataset size. Formal security analysis proves that our scheme can achieve Indistinguishability against Chosen-Plaintext Attack (IND-CPA), and extensive experiments prove that our scheme is feasible in real-world applications.

This paper studies a novel application of an aerial manipulator (AM)-the contact-based landing on a mobile platform. An AM is inherently unstable, under-actuated, and usually loses some DOFs while contacting environments. Meanwhile, the AM's flight state is susceptible to uncertain movements of the mobile platform, such as acceleration, sudden stopping, and reversing. To accomplish the contact-based landing mission, a robust controller is first designed to maintain a steady contact-based flight. Then a hierarchical control framework is applied, integrating the controllers in free-flight and restricted-flight stages. An AM and a mobile platform are developed for contact-based flight experiments. The proposed scheme is reliable and has good repeatability in experiments. To the best of our knowledge, this is the first time an AM has been implemented to conduct a contact-based landing, which is also an innovative landing approach for rotorcraft UAVs.

To solve the data island issue in the distributed Internet of Things (IoT) without privacy leakage, privacy-preserving federated learning (PPFL) has been extensively explored in both academic and industrial fields. However, existing PPFL solutions still suffer from a single point of failure and incur untrusted aggregation results caused by a malicious central server, and even cause a loss of model accuracy in an asynchronous setting. To solve these issues, we propose a privacy-preserving asynchronous federated learning scheme by using blockchain. Specifically, we use blockchain to address single points of failure and untrustworthy aggregation results, implement reliable model aggregation utilizing a practical byzantine fault-tolerant protocol in an asynchronous setting, and leverage differential privacy to improve system robustness. Formal security analysis and convergence analysis demonstrate that the proposed scheme is secure and robust, and extensive experiments demonstrate that our scheme can effectively ensure the accuracy of the system when compared with state-of-the-art schemes.

To achieve the search over encrypted data in cloud server, Searchable Encryption (SE) has attracted extensive attention from both academic and industrial fields. The existing Bloom filter-based SE schemes can achieve similarity search, but will generally incur high false positive rates, and even leak the privacy of values in Bloom filters (BF). To solve the above problems, we first propose a basicPrivacy-preservingBloom filter-basedKeywordSearch scheme using the Circular Shift and Coalesce-Bloom Filter (CSC-BF) and Symmetric-key Hidden Vector Encryption (SHVE) technology (namely PBKS), which can achieve effective search while protecting the values in BFs. Then, we design a new index structure T-CSCBF utilizing theTwin Bloom Filter (TBF) technology. Based on this, we propose an improved scheme PBKS+, which assigns a unique inclusion identifier to each position in each BF with privacy protection. Formal security analysis proves that our schemes are secure against Indistinguishability under Selective Chosen-Plaintext Attack (IND-SCPA), and extensive experiments using real-world datasets demonstrate that our schemes are feasible in practice.

With the rapid development of Intelligent Transportation System (ITS), a large number of spatial data are generated in ITS. Although outsourcing spatial data to the cloud server can reduce the high local computation and storage overheads, it will also lead to security and privacy issues. Therefore, it is necessary to have a survey to specifically summarize these advanced privacy-preserving spatial data query schemes. However, the existing surveys considering both location information and keywords of spatial data only summarize the spatial keyword query scheme in plaintext environment, they do not consider the privacy of spatial data. Although there are some surveys on privacy-preserving spatial data query, they only focus on the location information of spatial data without considering descriptive keywords. Therefore, to understand the progress and research trends in the field, we give a comprehensive survey on secure spatial data query in ITS to summarize and analyze the most advanced solutions. Then, we make a comprehensive and detailed comparison of existing solutions in terms of query function, index structure, time complexity, security, etc. Finally, we show some open challenges and potential research directions for privacy-preserving spatial data query.

Internet of Things (IoT) devices and systems are becoming increasingly commonplace, and as such systems scale up, so do the computational and storage requirements. Hence, one recent trend is to outsource data from IoT devices to remote systems. To facilitate both ciphertext retrieval and data confidentiality in the outsourced data, a number of searchable encryption (SE) approaches have been proposed in the literature. However, due to limited keyword space, a number of SE schemes are vulnerable to keyword guessing attacks (KGAs). In addition, existing SE approaches generally do not consider the hierarchical structure in which users at different levels require varying access privileges. Furthermore, existing SE schemes seldom provide time-controlled access control. Therefore, in this article, we propose a time-controlled hierarchical multikeyword search by using a double-server architecture to mitigate KGA. In our approach, we also build a public key tree to support different access permissions for hierarchical users. Formal security analysis shows that our scheme is secure, and extensive experiments demonstrate that our scheme is practical.

Cloud-assisted Industrial Internet of Things (IIoT) systems are increasingly deployed in various applications such as location-based services. Outsourcing data to cloud servers can help minimize local data storage and computation overheads, but it may introduce security and privacy concerns. Therefore, privacy-preserving spatial keyword search has been extensively explored in the literature. However, existing solutions still reveal the order of the spatio-textual similarity values between the query point and all data objects, and do not support searching for arbitrary geometric regions. To solve these issues, in this article we propose a privacy-preserving threshold spatial keyword search (TSKS) scheme. Specifically, we use the polynomial fitting technology, vector space model, and randomizable matrix multiplication technology to allow the cloud server to find relevant objects that are within some arbitrary geometric range and contain all query keywords. Finally, formal security analysis proves that our scheme can protect the privacy of data sets and queries, and extensive experiments demonstrate that our scheme is efficient and practical.

Glass curtain walls have been widely used in modern architecture. This makes it urgent to inspect and clean these glasses at regular intervals. Up to now, most of these work is performed by workers, which is expensive and inefficient. Therefore, a novel robot-the contact aerial manipulator system-is developed. The new designed system presents priorities in the aspects of high flexibility and easy operation. In this paper, the system mechanical structure is first introduced. Subsequently, the hybrid force/motion control framework is utilized to realize the precise and steady motion on the two-dimensional plane and maintain a certain sustained contact force, simultaneously. Finally, two flight experiments (including continuous square-wave trajectory tracking and aerial drawing task) are performed and the results indicate that the developed contact aerial manipulator works and presents good performance.

Contact-based operation in moving process is a challenging problem for aerial manipulators. It requires the whole system to maintain steady contact with external environment, to track some predefined trajectories on surfaces, and simultaneously to present some fixed contact force. Aiming at this problem, a hybrid force/motion control framework is proposed in this paper. In this framework, contact force control and position control are performed separately in two orthogonal subspaces: constrained space and free-flight space. To control the contact force, the closed-loop unmanned aerial vehicle is first theoretically shown to behave dynamically as a spring-mass-damper system. Further, an inverse-dynamics-based controller is proposed. To control the moving along the contact surface, trajectory planning and position controller are combined to achieve the steady behavior in a free-flight subspace. In the end, an aerial manipulator system with a roller-type end-effector was designed, and practical flight experiments was performed. The results indicate that the proposed framework is effective and validity.

The dangerous work situation in industrial leakage accidents urgently needs a flexible and small robot to help workers perform operations and to protect them from being injured. An aerial manipulator system consisting of a hexa-rotor UAV and a one-DOF manipulator is developed, and is used to press an emergency switch to shut off machinery in an emergency. In practical application, an aerial manipulator usually performs contact operations as the UAV platform is in hover flight. The hovering UAV acting as a spring-mass-damper system is firstly proved. Then, based on the derived spring-mass-damper system model and the impedance control algorithm, the force-sensorless contact force control method is presented. That is, the force is indirectly controlled through controlling the UAV's position error and pitch angle simultaneously. The practical operation experiment of pressing an emergency button shows that the proposed method is able to control the contact force as the aerial manipulator interacts with the external environment.

Space-time adaptive processing (STAP) techniques have achieved good performance when applied to side-looking airborne radar (SLAR) where the ground clutter is relatively stationary, but due to severe range dependence, the performance is not so good in non-SLAR particularly when the pulse repetition frequency is high enough to induce range ambiguity. Because the short-range clutter Doppler for non-SLAR varies rapidly with range while the long-range clutter Doppler varies gently, at the presence of range ambiguity, the clutter in different ambiguous ranges does not coincide. The clutter range dependence mitigation for the case of range ambiguity is the major problem that this letter intends to solve. A subarray synthesis algorithm with prefiltering in elevation is presented. By this technique, the ambiguous short-range clutter is eliminated before STAP; therefore, the clutter range dependence is alleviated, and then, the STAP performance will be improved greatly.