Micromanipulation using magnetic microswarms has garnered significant attention in recent years due to their potential in microscale cargo delivery tasks. While existing studies have demonstrated the capabilities of microswarms in basic manipulation tasks, they often lack the autonomy required to handle more complex specifications, particularly temporal logic tasks. In this paper, we propose a novel formal planning strategy for magnetic microswarms that enables cargo delivery in complex environments while satisfying finite linear temporal logic (LTLf) specifications. Our approach consists of two key components. First, we develop a high-level path planner based on a bidirectional temporal logic rapid-explore random tree star (BTL-RRT*) algorithm, which facilitates efficient planning while ensuring compliance with the given task specifications. Second, we employ an automaton to manage the manipulation modes of the microswarm, enabling real-time control over the capture and release of cargoes. In addition, we implement the planning strategy on microswarms actuated by a visual feedback magnetic tweezers system. Extensive simulations and experimental results demonstrate the effectiveness of the proposed planning strategy. The results indicate that, using the proposed approach, microswarms can autonomously select and deliver multiple microbeads to designated regions in both static and dynamic environments, adhering to the LTLfspecifications.

Signal Temporal Logic (STL) is a powerful specification language for describing complex temporal behaviors of continuous signals, making it well-suited for high-level robotic task descriptions. However, generating executable plans for STL tasks is challenging, as it requires consideration of the coupling between the task specification and the system dynamics. Existing approaches either follow a model-based setting that explicitly requires knowledge of the system dynamics or adopt a task-oriented data-driven approach to learn plans for specific tasks. In this work, we address the problem of generating executable STL plans for systems with unknown dynamics. We propose a hierarchical planning framework that enables zero-shot generalization to new STL tasks by leveraging only task-agnostic trajectory data during offline training. The framework consists of three key components: (i) decomposing the STL specification into several progresses and time constraints, (ii) searching for timed waypoints that satisfy all progresses under time constraints, and (iii) generating trajectory segments using a pre-trained diffusion model and stitching them into complete trajectories. We formally prove that our method guarantees STL satisfaction, and simulation results demonstrate its effectiveness in generating dynamically feasible trajectories across diverse long-horizon STL tasks. Project Page: https://cps-sjtu.github.io/Zero-Shot-STL/

In this paper, we present a method for synthesizing optimal policies for stochastic systems under high-level mission specifications while maintaining information security. The stochastic systems are modeled as probabilistic labeling Markov decision processes (PLMDPs), and the high-level mission specifications are represented by linear temporal logic (LTL) formulas, which must be satisfied repeatedly, infinitely often. Meanwhile, an intruder is modeled as an observer who knows the exact structure of the system and passively monitors its behavior, aiming to infer the high-level mission specifications. Our objective is to synthesize an optimal policy that minimizes the mean payoff cost while preventing the intruder from definitively determining the given mission specifications. We begin by extending the PLMDP model by incorporating an observation function and the corresponding probability function, and we introduce the concept of LTL opacity for stochastic systems. Next, by proposing a new structure called the synchronous accepting maximally end component, we identify the subset of the accepting end components that satisfy the LTL opacity requirement. Finally, we develop a modified linear program with information security constraints to synthesize an optimal policy that ensures both formal correctness and LTL opacity.

This paper aims to explore the problem of verifying detectability for unambiguous weighted automata (UWAs) through the utilization of modified self-composition. Specifically, we focus on two types of detectability: strong periodic detectability (SPD) and strong D-detectability (SDD). The problem involves periodically determining the current state or distinguishing certain state-pairs of the system, based on the occurrence of a finite number of observable events. We introduce a new polynomial-time algorithm different from the detector, called self-composition for UWAs, and prove that it can be used to verify the SPD and SDD for UWAs. Ultimately, we propose necessary and sufficient conditions based on modified self-composition techniques to verify the aforementioned detectabilities for the studied UWA.

Monocular depth estimation is a fundamental task in computer vision and multimedia. The self-supervised learning pipeline makes it possible to train the monocular depth network with no need of depth labels. In this paper, a multi-frame depth model with multi-scale feature fusion is proposed for strengthening texture features and spatial-temporal features, which improves the robustness of depth estimation between frames with large camera ego-motion. A novel dynamic object detecting method with geometry explainability is proposed. The detected dynamic objects are excluded during training, which guarantees the static environment assumption and relieves the accuracy degradation problem of the multi-frame depth estimation. Robust knowledge distillation with a consistent teacher network and reliability guarantee is proposed, which improves the multi-frame depth estimation without an increase in computation complexity during the test. The experiments show that our proposed methods achieve great performance improvement on the multi-frame depth estimation.

In this article, the finite-time cooperative control problem for leader-following bearing-defined formation tracking of multiagent systems with double-integrator dynamics is investigated. Different from the existing works on finite-time containment control, our objective is to make followers track leaders' trajectories and form a shape-preserving formation rather than a convex hull. The target formation is defined by both leaders' motions and bearing constraints among neighboring agents, which enables the formation not only to form and preserve a geometric pattern but also to have the ability to achieve both translational and scaling formation maneuver. To satisfy the bearing constraints, a matrix-weighted estimator/controller is developed. The finite-time stabilization of the target formation is achieved, though the matrix-weighted design makes the stability analysis complicated. Finally, an illustrative example is presented to demonstrate the effectiveness.

In this paper, the distributed rendezvous control problem of networked uncertain robotic systems with bearing measurements is investigated. The network topology of the multi-robot systems is described by an undirected graph. The dynamics of robots is modeled by Euler-Lagrange equation with unknown inertial parameters, which is more general than simple kinematics considered in existing works on rendezvous problem of multi-robot systems. To achieve rendezvous, a distributed adaptive force/torque control law is developed for each robot, which uses bearings with respect to its neighbors instead of relative displacements or distances. It is shown that the resulting closed-loop multi-robot systems are globally asymptotically stable. Then, the rendezvous control problem of multiple wheeled mobile robots is further solved by the proposed approach. Finally, on-site experiment on networked TurtleBot3 Burger mobile robots is conducted and the results demonstrate effectiveness of the proposed approach.

This paper investigates the cooperative target enclosing of multiple unicycle-type mobile robots subject to input disturbances. The objective is to make all robots orbit around a given stationary target, and maintain evenly spaced along a common circle. The network of the multirobot systems is set in a cyclic pursuit manner. A dynamic control law is developed for the cooperative target enclosing of the multirobot systems, while tackling the heterogeneous input disturbances generated by linear exogenous systems. The proposed control law requires each robot to use the relative displacement measurements with respect to the target and its neighbors. It is shown that global asymptotic stability of the closed-loop multirobot systems can be guaranteed in the presence of a large class of input disturbance signals. Finally, simulation results illustrate the effectiveness of our approach.

This paper investigates the cooperative moving-target enclosing control problem of networked unicycle-type nonholonomic vehicles with constant linear velocities. The information of the target is only known to some of the vehicles, and the topology of the vehicle network is described by a directed graph. A dynamic control law is proposed to steer the vehicles, such that they can get close to orbiting around the target while the target is moving with a time-vary velocity. Besides, the constraint of bounded angular velocity for the vehicles can always be satisfied. The proposed control law is distributed in the sense that each vehicle only uses its own information and the information of its neighbors in the network. Finally, simulation results of an example validate the effectiveness of the proposed control law.

In this paper, a novel path planning strategy based on fuzzy logic for a robot arm with a fixed pedestal is proposed, and the simulation environment conditions in which the robot arm works are specific and unpredictable. First, considering a simple working platform set in front of the robot arm system, it will become an obstacle when robot is working. Second, an obstacle avoidance path planning strategy is designed and the coordinates of robot arm end are used to achieve close-loop control. In addition, fuzzy control theory is applied to design a fuzzy path planning controller, and a dangerous sign is introduced to avoid obstacle. Finally, the experiments simulated on a two-joint robot arm with a fixed pedestal is given. The results show the feasibility and safety of the proposed path planning strategy.

We investigate how to estimate information trustworthiness by considering multiple information sources jointly in a latent matrix space. We particularly focus on user review and recommendation systems, as there are multiple platforms where people can rate items and services that they have purchased, and many potential customers rely on these opinions to make decisions. Information trustworthiness is a serious problem because ratings are generated freely by end-users so that many stammers take advantage of freedom of speech to promote their business or damage reputation of competitors. We propose to simply use customer ratings to estimate each individual source's reliability by exploring correlations among multiple sources. Ratings of items are provided by users of diverse tastes and styles, and thus may appear noisy and conflicting across sources, however, they share some underlying common behavior. Therefore, we can group users based on their opinions, and a source is reliable on an item if its opinions given by latent groups are consistent across platforms. Inspired by this observation, we solve the problem by a two-step model -- a joint matrix factorization procedure followed by reliability score computation. We propose two effective approaches to decompose rating matrices as the products of group membership and group rating matrices, and then compute consistency degrees from group rating matrices as source reliability scores. We conduct experiments on both synthetic data and real user ratings collected from Orbitz, Priceline and Trip Advisor on all the hotels in Las Vegas and New York City. Results show that the proposed method is able to give accurate estimates of source reliability and thus successfully identify inconsistent, conflicting and unreliable information.