Vector fields are advantageous in handling non holonomic motion planning, as they provide the robot with reference orientation across the workspace. However, additionally incorporating curvature constraints presents challenges due to the interconnection between the design of the curvature-bounded vector field and the tracking controller under limited actuation. In this paper, we present a novel framework to co-develop the vector field and the control law, guiding the nonholonomic robot to the target configuration with curvature-bounded trajectory. First, we formulate the problem by introducing the target positive limit set, which allows the robot to either converge to or pass through the target configuration, depending on its dynamics and the specific tasks. Next, we construct a curvature-constrained vector field (CVF) via blending and embedding elementary flows in the workspace. To track such CVF, a saturated control law with dynamic gains is proposed, under which the tracking error's magnitude decreases even when saturation occurs. Under the control law, the kinematically constrained nonholonomic robot is guaranteed to track the reference CVF and converge to the target positive limit set with bounded trajectory curvature. Numerical simulations show that the proposed CVF method outperforms other vector-field-based algorithms. Experiments on Ackermann UGVs and semi-physical fixed-wing UAVs demonstrate that the method can be effectively implemented in real-world scenarios.

For formation control systems based on wireless communications, the communication interactions and control performance are highly coupled, while traditional methods do not take this coupling into account. To fill this gap, we study the formation control problem of second-order multiagent systems (MASs) with uncertain dynamics (affected by unknown but bounded (UBB) process noises) and commonly used time division multiple access (TDMA) communications. First, we establish the communication topology among agents as a periodically switching topology governed by precise switching mechanisms, which characterizes the effects of the TDMA mechanism, agent dynamics, and the physical layer of communications. Based on the communication model, we propose a control-communication codesign for MASs, where the delay-based formation controller and the distributed transmit power control algorithm are developed, to guarantee the bounded formation stability of control systems and reliable connections of the desired communication links simultaneously. The codesign framework systematically integrates formation control and communication transmission under physical layer constraints and provides a paradigm to better characterize the coupling relationship between communication interactions and control performance. Finally, experiments using realistic communication characteristics are conducted to validate the effectiveness of our codesign method.

As the airspace becomes increasingly congested, decentralized conflict resolution methods for airplane encounters have become essential. While decentralized safety controllers can prevent dangerous midair collisions, they do not always ensure prompt conflict resolution. As a result, airplane progress may be blocked for extended periods in certain situations. To address this blocking phenomenon, this paper proposes integrating bio-inspired nonlinear opinion dynamics into the airplane safety control framework, thereby guaranteeing both safety and blocking-free resolution. In particular, opinion dynamics enable the safety controller to achieve collaborative decision-making for blocking resolution and facilitate rapid, safe coordination without relying on communication or preset rules. Extensive simulation results validate the improved flight efficiency and safety guarantees. This study provides practical insights into the design of autonomous controllers for airplanes.

Human drivers naturally balance the risks of different concerns while driving, including traffic rule violations, minor accidents, and fatalities. However, achieving the same behavior in autonomous driving systems remains an open problem. This paper extends a risk metric that has been verified in human-like driving studies to encompass more complex driving scenarios specified by linear temporal logic (LTL) that go beyond just collision risks. This extension incorporates the timing and severity of events into LTL specifications, thereby reflecting a human-like risk awareness. Without sacrificing expressivity for traffic rules, we adopt LTL specifications composed of safety and co-safety formulas, allowing the control synthesis problem to be reformulated as a reachability problem. By leveraging occupation measures, we further formulate a linear programming (LP) problem for this LTL-based risk metric. Consequently, the synthesized policy balances different types of driving risks, including both collision risks and traffic rule violations. The effectiveness of the proposed approach is validated by three typical traffic scenarios in Carla simulator.

Cyber-physical systems (CPSs) facilitate the integration of physical entities and cyber infrastructures through the utilization of pervasive computational resources and communication units, leading to improved efficiency, automation, and practical viability in both academia and industry. Due to its openness and distributed characteristics, a critical issue prevalent in CPSs is to guarantee resilience in the presence of node injection attack. This survey provides a detailed and comprehensive overview of recent advances on resilient coordination against node injection attacks for CPSs based on the idea ofmean-subsequence-reduction(MSR). Different from existing survey papers that adopt a horizontal taxonomy based on attack type, we propose a vertical taxonomy according to the multi-layered framework of CPS, which enables a more in-depth of the latest developments in MSR-based resilient coordination results. Furthermore, the proposed taxonomy fills the gap that no survey has gathered and classified numerous MSR-type papers from the perspective of system structure. Specifically, more than one hundred MSR-based resilient coordination results are classified and reviewed from three perspectives: physical structure, network topology, and communication mechanism. Finally, three typical application scenarios, i.e., resilient frequency synchronization for single-phase microgrids, resilient containment for multi-robot systems, and resilient distributed optimization for multi-microgrid systems are examined, respectively, all of which demonstrate the applicability of the reviewed MSR-based strategies.

Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment. Nevertheless, the reward function for an RL agent, which is significant to its performance, is challenging to determine. The conventional work mainly focuses on rewarding safe driving states but does not incorporate the awareness of risky driving behaviors of the vehicles. In this paper, we investigate how to use risk-aware reward shaping to leverage the training and test performance of RL agents in autonomous driving. Based on the essential requirements that prescribe the safety specifications for general autonomous driving in practice, we propose additional reshaped reward terms that encourage exploration and penalize risky driving behaviors. A simulation study in OpenAI Gym indicates the advantage of risk-aware reward shaping for various RL agents. Also, we point out that proximal policy optimization (PPO) is likely to be the best RL method that works with risk-aware reward shaping.

In this article, we investigate distributed consensus seeking with multiple convergence performance requirements for single-integrator multiagent systems under undirected graphs. A unified distributed control framework is proposed to ensure consensus or practical consensus as well as performance time requirements, which contains most existing complex protocol schemes as special cases. In the proposed framework, three functions with specific properties in the controller play different roles and can be freely designed to achieve desired convergence performances, which guarantee a high-level scalability for multiple control requirements in addition to convergence time. For highlighting the compatibility and flexibility of the proposed method, four typical scenarios are discussed to reach exponential, finite-time, fixed-time, and appointed-time consensus seeking, respectively. Finally, numerical simulations are carried out to verify the effectiveness of the theoretical analysis.

Graph matching, or the determination of the vertex correspondences between a pair of graphs, is a crucial task in various problems in different science and engineering disciplines. This article aims to propose a distributed optimization approach for graph matching (GM) between two isomorphic graphs over multiagent networks. For this, we first show that for a class of asymmetric graphs, GM of two isomorphic graphs is equivalent to a convex relaxation where the set of permutation matrices is replaced by the set of pseudostochastic matrices. Then, we formulate GM as a distributed convex optimization problem with equality constraints and a set constraint, over a network of multiple agents. For arbitrary labelings of the vertices, each agent only has information about just one vertex and its neighborhood, and can exchange information with its neighbors. A projected primal-dual gradient method is developed to solve the constrained optimization problem, and globally exponential convergence of the agents' states to the optimal permutation is achieved. Finally, we illustrate the effectiveness of the algorithm through simulation examples.

In this article, we propose coordinating guiding vector fields to achieve two tasks simultaneously with a team of robots: first, the guidance and navigation of multiple robots to possibly different paths or surfaces typically embedded in 2-D or 3-D, and second, their motion coordination while tracking their prescribed paths or surfaces. The motion coordination is defined by desired parametric displacements between robots on the path or surface. Such a desired displacement is achieved by controlling the virtual coordinates, which correspond to the path or surface's parameters, between guiding vector fields. Rigorous mathematical guarantees underpinned by dynamical systems theory and Lyapunov theory are provided for the effective distributed motion coordination and navigation of robots on paths or surfaces from all initial positions. As an example for practical robotic applications, we derive a control algorithm from the proposed coordinating guiding vector fields for a Dubins-car-like model with actuation saturation. Our proposed algorithm is distributed and scalable to an arbitrary number of robots. Furthermore, extensive illustrative simulations and fixed-wing aircraft outdoor experiments validate the effectiveness and robustness of our algorithm.

In this article, a novel design scheme is introduced to solve the optimal control problem for nonlinear systems with unsymmetrical and state-dependent input constraints. By introducing an initial stabilizing control policy as the baseline of the constructed optimal control policy, we remove the assumption in the current study for the adaptive optimal control, that is, the internal dynamics should hold zero when the state of the system is in the origin. Particularly, nonlinear control systems with partially unknown dynamics are investigated and the procedure to acquire the corresponding optimal control policy is presented. The stability for the closed-loop dynamics and the optimality of the obtained control policy are both proved. Besides, we apply the proposed control design framework to solve the optimal circumnavigation problem based on the accumulative Fisher information for a fixed-wing unmanned aerial vehicle (UAV). The control performance of our algorithm is compared with that of the existing circumnavigation control policy in a numerical simulation.

This paper studies the distance-based formation control problem where agents in the system are modeled with single-integrators with parametric uncertainties. We propose to use adaptive control as the main tool to approach the problem. Under the assumption that only local displacements are available, an adaptive distance-based control law is proposed and analysed. If additionally, each agent can sense its local position, we propose using an additional observer to estimate the uncertain parameters and stabilize the desired formation. Simulation results are also given to support the theoretical results.

This work investigates the fixed-time distributed coordination control for multiple Euler-Lagrange systems and, in particular, addresses containment control with stationary/dynamic leaders as well as leaderless synchronization control. For the containment control scenario with stationary leaders, the subgraph associated with followers is directed. When dynamic leaders are involved, the information transfer between neighboring followers is bidirectional, for which a novel distributed estimator is developed. For the leaderless synchronization control scenario, the communication network among agents is unidirectional. Three fixed-time distributed control schemes are designed for the aforementioned three cases by applying the fixed-time stability theory. The convergence of the coordination control objectives can be achieved in a fixed time that does not depend on any initial conditions of agents' states, and the settling times are also explicitly derived. Finally, numerical simulations are presented to demonstrate the feasibility of the developed control strategies.

It is essential in many applications to impose a scalable coordinated motion control on a large group of mobile robots, which is efficient in tasks requiring repetitive execution, such as environmental monitoring. In this paper, we design a guiding vector field to guide multiple robots to follow possibly different desired paths while coordinating their motions. The vector field uses a path parameter as a virtual coordinate that is communicated among neighboring robots. Then, the virtual coordinate is utilized to control the relative parametric displacement between robots along the paths. This enables us to design a saturated control algorithm for a Dubins-car-like model. The algorithm is distributed, scalable, and applicable for any smooth paths in an n-dimensional configuration space, and global convergence is guaranteed. Simulations with up to fifty robots and outdoor experiments with fixed-wing aircraft validate the theoretical results.

This article discusses cooperative stabilization control of rigid formations via an event-based approach. We first design a centralized event-based formation control system, in which a central event controller determines the next triggering time and broadcasts the event signal to all the agents for control input update. We then build on this approach to propose a distributed event control strategy, in which each agent can use its local event trigger and local information to update the control input at its own event time. For both cases, the triggering condition, event function, and triggering behavior are discussed in detail, and the exponential convergence of the event-based formation system is guaranteed.

In this paper, we develop some new event-triggered algorithms for achieving distributed consensus for a multiagent system that guarantees fully Zeno-free triggerings for all the agents. In the proposed framework, each agent updates its control input only at its own triggering instants by using local measurements (i.e., relative states) with respect to neighboring agents, and such local measurements can be done in a local coordinate frame. For all agents, a positive Lpsignal function is embedded in the event detector functions, which aims to avoid the possible comparison of an event error term to a zero threshold that may happen in a zero-crossing scenario. We further propose a Zeno-free self-triggered algorithm to achieve multiagent consensus, which enables discrete-time measurements and thus avoids continuous measurements between the neighboring agents. We also show that the proposed event-based consensus algorithms guarantee less frequent triggered events in a bounded time interval compared to the conventional algorithm without Lpsignals. Simulations and comparisons are provided to validate the performance and effectiveness of the proposed event-based consensus schemes.

We consider the problem of feasible coordination control for multiple homogeneous or heterogeneous mobile vehicles subject to various constraints (nonholonomic motion constraints, holonomic coordination constraints, equality/inequality constraints etc). We develop a general framework involving differential-algebraic equations and viability theory to describe and determine coordination feasibility for a coordinated motion control under heterogeneous vehicle dynamics and various constraints. A heuristic algorithm is proposed for generating feasible trajectories for each individual vehicle. We show several application examples and simulation experiments on multi-vehicle coordination under various constraints to validate the theory and the effectiveness of the proposed algorithm and control schemes.

In this paper we propose an algorithm for stabilizing circular formations of fixed-wing UAVs with constant speeds. The algorithm is based on the idea of tracking circles with different radii in order to control the inter-vehicle phases with respect to a target circumference. We prove that the origin of the error dynamics of the inter-vehicle phases is exponentially stable. Furthermore, thanks to the stability properties of the guidance vector field that guides the vehicles, the algorithm can be extended to other closed trajectories. One of the main advantages of this approach is that the algorithm guarantees the confinement of the team in a specific area, even when communications or sensing among vehicles are lost. We show the effectiveness of the algorithm with an actual formation flight of three aircraft. The algorithm is ready to use for the general public in the open-source Paparazzi autopilot.