BACKGROUND: CARLIN and DNA Typewriter are two major breakthroughs in CRISPR-based lineage tracing technology. It is essential to understand the potential and performance of these methods in lineage tracing, which provides important guidance on experimental design. RESULTS: In this study, we systematically compare these two strategies using a unified stochastic simulation framework with known ground-truth lineages. By explicitly modeling CRISPR editing dynamics, barcode evolution, and cell division processes, the framework enables quantitative benchmarking of lineage reconstruction accuracy across diverse experimental parameter regimes. Both methods are evaluated using multiple accuracy metrics, including Robinson-Foulds accuracy and triplet accuracy, allowing a comprehensive assessment of lineage reconstruction performance under various editing probabilities, sampling depths, and lineage lengths. CONCLUSIONS: DNA Typewriter consistently outperforms CARLIN in lineage reconstruction accuracy when sufficient numbers of recording targets are used, particularly in more cell divisions. Sequential and ordered recording in DNA Typewriter substantially reduces ambiguity in lineage inference compared to unordered CRISPR barcode editing. CARLIN's lineage-recording potential exhausts rapidly under continuous induction, limiting its effectiveness in long-term lineage tracing. Triplet accuracy provides a more permissive and informative metric than Robinson-Foulds accuracy, especially under partial sampling scenarios.

Since collision can change the velocity in a very short time, a collision-inclusive trajectory planning algorithm for unmanned aerial vehicle (UAV) can utilize collision to get a fast and energy-efficient trajectory in complex environments. We proposed a lightweight collision-inclusive trajectory planner, which can be integrated into a UAV system easily. The trajectory segments that need to be optimized are recognized by the curvature of the collision-free trajectory. After getting the pre-collision information by forward integration of sampled control, the optimal collision position and time will be generated by the collision-inclusive optimizer in 40ms. The experiments verify the effectiveness and efficiency of our method.

In this study, we analyze a multi-UAV target-attacker-defender (TAD) differential game framework in which multiple defenders is tasked with shielding a target from several attackers. The attackers are driven by the objective of seizing the target, while the defenders focus on intercepting their advances. Hierarchical reinforcement learning (HRL) framework offers a promising strategy for the TAD problem. This work introduces a two-level goal-conditioned HRL method. At the first level, defenders are dynamically paired with target attackers through an assignment mechanism guided by differential game theory, where optimal intercept trajectories are computed to inform the matching process. The second level employs a multi-agent deep deterministic policy gradient (MADDPG) algorithm to derive coordinated policies for both teams. Experimental validation demonstrates the framework’s effectiveness compared to baseline method.

This paper proposes a prescribed-time disturbance estimation method for nonlinear systems with unknown nonlinear disturbances, such as aerial manipulators. Taking full account of the fast convergence characteristics of prescribed-time theory and the stability and realizability of the Extended State Observer (ESO) in nonlinear disturbance estimation, this paper integrates the two by introducing bounded time-varying gains. This approach solves the spike problem of traditional ESO at the initial moment and extends the applicability of prescribed-time theory to the full time interval, effectively enhancing the real-time performance and accuracy of disturbance estimation. The effectiveness of the proposed method is verified through simulations.

Autonomous tracking of dynamic targets by unmanned ground vehicles (UGVs) is a crucial problem in various applications. However, existing approaches often suffer from low computational efficiency, inadequate handling of target visibility and limited adaptability to the rapid motion of the target. To address these limitations, this paper proposes a planner that combines target prediction-based topological path generation with a spatial-temporal trajectory optimization method. The future topological paths of target are predicted via motion primitives, and local topological search generates multiple candidate tracking paths for the tracker. Then an optimal path is selected on the basis of visibility cost, energy cost, and topological consistency. The spatial-temporal optimization method generates a smooth, dynamically feasible trajectory with minimum squared jerk and time. Considering both tracking distance and visibility constraints, the effectiveness of the proposed method we validate is through various simulation experiments.

This paper introduces a novel hybrid unmanned aerial vehicle (UAV) configuration named as the hybrid QuadPlane with all-moving wings (HQWAW), which features two wings capable of dynamic adjustment during flight. Compared with conventional QuadPlane, the HQWAW can optimize lift generation and reduce rotor thrust by altering its angle of attack. But the additional degrees of freedom and nonlinear dynamics pose challenges for control strategy design. We proposed an end-to-end control strategy using deep reinforcement learning (DRL). This approach enables the HQWAW to discover an optimal control policy that simultaneously improve tracking accuracy and energy efficiency, with the simulation results illustrating the effectiveness of the proposed method.

This article proposes a novel safety-critical control (SCC) framework based on embedded control barrier functions (EMB-CBF-SCC) for high-order strict-feedback nonlinear affine control systems. It is aimed at reconciling the potential conflict between predesigned desired trajectory and multiple safety constraints that could have different high relative degrees. Compared with existing CBF-based SCCs, our method can significantly reduce differential order and computational burden. Specifically, we first propose a novel concept of embedded control barrier function (EMB-CBF), which can reduce an arbitrary high-relative-degree safety constraint to relative degree one, and ensure safety of high-relative-degree systems. Further, EMB-CBF-SCC divides the original system into a top-level and a bottom-level subsystem. Then, it embeds between the two subsystems a quadratic program based on EMB-CBF, and introduces command filters to smooth virtual control inputs and obtain differential signals. Coordination performance of safety and stability is analyzed, considering the impact of filter errors. Finally, we present two real safety-critical robotic application scenarios with different safety constraint settings, namely, multiple state constraints for a single-link manipulator numerical model and dynamic obstacle avoidance constraints for a self-developed micro mobile robot experimental platform, respectively. The effectiveness of the proposed framework is demonstrated in both scenarios.

Thrust-vectoring multirotor is the current cuttingedge focus of research in the field of multirotor. This technology promises advancements such as over-actuated dynamics, decoupling of position and attitude control, and the ability to track 6-Dof trajectories. Building upon our previous work on thrust-vectoring quadrotor, this paper introduces a novel thrust-vectoring hexarotor configuration. We propose a nonlinear generalized predictive control method utilizing the super-twisting extended state observer (STESO). The stability of our closed-loop system is rigorously established through theoretical analysis. To validate the practical applicability of our approach, simulation for 6-Dof trajectory tracking of the thrust-vectoring hexarotor is conducted using sophisticated high-fidelity dynamics simulation software. This simulation serve to affirm the effectiveness of our proposed system in achieving precise trajectory control and maneuverability in various flight scenarios.

In the industrial Internet of Things (IIoT) scenarios, data sharing can promote mutual collaboration among production parties to improve productivity and optimise resource allocation, but data sharing in industrial scenarios faces the risk of privacy leakage due to open networks. Attribute-based encryption (ABE) can be used to solve the problem of data sharing privacy leakage. However, there is still no effective privacy protection solution for data sharing and access control policies in blockchain, which may expose sensitive information of data owners. Moreover, existing schemes only focus on protecting data privacy and ignore the protection of users’ query privacy. In addition, most of these schemes are based on cloud servers, which may lead to data tampering and a single point of failure. To address these issues, we propose a blockchain-based data security sharing scheme (BAPIR) that combines attribute-based encryption and private information retrieval (PIR). In this paper, we implement fine-grained access control using ABE based on the inner product and protecting the access policy. Additionally, PIR technology is introduced to protect users’ query privacy. To achieve efficient blockchain data storage, IPFS is used for on-chain and off-chain collaboration, and complex computational tasks are outsourced to edge servers, ensuring secure and efficient data sharing. Finally, we demonstrate the security and efficiency of BAPIR through security analysis and performance evaluation.

Aerial manipulator is a kind of robot with broad application prospects, which is suitable for high altitude operation and other dangerous application scenarios. This paper presents a trajectory tracking control algorithm for the aerial parallel manipulator based on Stewart platform. By modeling the overall dynamics of the aerial parallel manipulator, the expression of the influence of Stewart platform is given, and it is proved that this type of influence can be combined with the unmodeled error and external disturbance into the comprehensive disturbance of the flight platform. The flight platform trajectory tracking control is carried out by using the backstepping method, and the nonlinear disturbance observer is used for disturbance estimation and compensation in the control output. Numerical experiments show that the proposed control method can realize the trajectory tracking control of the flight platform of the aerial parallel manipulator.

The target of this article is to design a data-driven model predictive control algorithm for general multirate sampled-data systems. Multirate sampling widely exists in the industrial process control systems. In this paper, not only sampling periods between inputs and outputs are different, but also periods among inputs or outputs are different from each other. For such the general multirate sampled-data system, we combine the lifting technique and partial least square method to obtain inputs/outputs data sets, which are used for the model regression. An incorporating autoregressive exogenous (ARX) structure model is utilized to model predict. Then we give the principal components cost function for the model predictive control algorithm. Finally, we use example to illustrate the ARX model's precision and the efficiency of our data-driven model predictive control algorithm for general multirate sampled-data systems.

This paper proposes a generalized varying-domain optimization method for fuzzy goal programming incorporating multiple priorities. According to the three possible styles of the objective function, the varying-domain optimization method and its generalization are corresponding proposed. In contrast to the previous method, the proposed method can make that the higher priority achieving the higher satisfaction degree. In this way, the decision-maker can get the optimal solution as well as guarantee the priorities of the multiple objective optimization problem. We demonstrate the power of this proposed method by three illustrative examples and a practice application.