This paper deals with the problem of dynamic trajectory planning for a group of unmanned aerial vehicles (UAVs) in unknown environments. The existing methods often suffer from excessive computational burden, which creates the gap between theoretical approaches and practical swarm deployment. To overcome these limitations, this paper proposes a distributed cooperative planning system (DCPS). The system consists of two levels: a trajectory planning level and a cooperative trajectory planning level. On the trajectory planning level, a kinodynamic Gaussian potential B-spline (KGPB) approach is designed by combining the local kinodynamic-A-star method and the Gaussian potential field B-spline method. Specifically, in the front-end, a trajectory is first generated by using the local kinodynamic-A-star method based on kinematics-dynamics constraints. And then, in the back-end, the trajectory is further optimized by using the Gaussian potential B-spline (GPB) method. On the cooperative trajectory planning level, a back field neighbor replanning (BFNP) approach is proposed, where each UAV only needs to communicate with the nearest neighbors. According to the potential collision region of the front UAV, the trajectory of the current UAV is dynamically improved to ensure safe and stable flight such that communication costs are significantly improved. Finally, the simulation results demonstrate that the proposed DCPS achieves at least a 27% increase in average velocity and a 24% reduction in traversal time for the UAV swarm compared to prior methods. The experimental outcomes provide further validation that the proposed DCPS can generate efficient and safe trajectories. For particular cases, the UAV operates at 97% of its maximum possible velocity. The proposed DCPS provides a reliable solution for a group of unmanned aerial vehicles in unknown environments.

In this paper, we propose a multi-agent reinforcement learning approach to address the design problem of control strategy for multi-agent consensus. Distributed control policy is learned automatically through reinforcement learning algorithm, thereby circumventing the intricate process of controller design. To be specific, the proposed approach exhibits notable consistency performance in scenarios wherein the agents’ initial positions differs from that in the training environment. We confirm the effectiveness of our approach by using several initial positions of agents and different dimensionality of positions in the simulations.

In this paper, two technologies are proposed to deal with the problem of flight safty of multiple unmanned aerial vehicles (UAVs) in unknown environments. One technology is to optimize the front-end path generated by traditional path planning methods in order to better match the dynamics of UAVs to obtain the back-end movement trajectories of UAVs. The other technology is to introduce the collision detection adjustment region such that collision avoidance can be realized for multiple UAVs by dynamic replanning of UAV’s trajectory under local neighborhood communication. Finally, according to simulation and real-world experimental results, the effectiveness of the proposed technologies is verified for the flight safty of multiple UAVs in unknown environments.

This article deals with the problem of target tracking and detecting in unknown environments by designing two new algorithms for an autonomous aerial vehicle (AAV). First, an auto-Gaussian-GRU-predictive (AGUP) algorithm is designed to solve the tracking problem of a dynamic target in unknown environments. By integrating Gaussian process regression and gated recurrent unit neural networks, the AGUP algorithm can predict the motion trajectory of a dynamic target. Second, a Tabu search interpolated B-spline (TBL) algorithm is also proposed to solve the problem of optimal path planning for multiple stationary targets. The TBL algorithm can efficiently plan the visiting paths and also can enable the path smooth. Third, both AGUP and TBL algorithms are combined with the model predictive control (MPC) approach in order to guide AAVs to track and detect the targets. Finally, simulation and experimental results show that the AGUP-MPC algorithm exhibits excellent tracking capability. In addition, the TBL-MPC algorithm effectively plans the optimal and smooth detection path and controls AAVs to orderly visit multiple stationary targets.

In this paper, the real-time motion planning framework for unmanned aerial vehicle (UAV) is proposed to solve the dynamic target tracking problem in complex environments. The framework is applicable to 3D path planning and trajectory optimization of UAV, which can effectively reduce the unsmoothness during UAV flight and achieve real-time tracking of dynamic targets in 3D space. The framework is divided into two parts: the front-end uses a graph search-based method to find the shortest path for the UAV to approach the dynamic target, while the back-end uses the GP (Gaussian Potential)-B-Spline soft-constrained trajectory optimization method to optimize the shortest path of the front-end, and design the trajectory that conforms to the motion for the UAV. The results show that the method exhibits excellent tracking performance in complex environments and has a wide potential for practical applications.

This paper mainly considers the navigation problem of unmanned aerial vehicle (UAV) in an unknown environment. Traditional path planning method relies on accurate model parameters and environment maps, which has poor adaptability. Therefore, this paper adopts the deep reinforcement learning algorithm to accomplish the navigation task. The classical proximal policy optimization (PPO) algorithm lacks the perception of the correlation between UAV action and state makes difficult for the UAV to choose the optimal path, thus affecting the success rate and speed of navigation. To solve this problem, this paper adds a long short-term memory (LSTM) network to the policy and evaluation network of the PPO algorithm so that the UAV can refer to the preceding status and action information during path planning. The method is extended to three-dimensional motion space. Simulation results demonstrate that the LSTM-PPO algorithm designed in this paper can complete navigation tasks in unknown environments, and show stability in continuous state space and continuous action space. Meanwhile, compared with the PPO algorithm, the success rate of navigation and average arrival time is significantly improved.

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.

This article addresses the problem of cooperative control of double-integrator type multirobot systems. Different from some conventional results with the control gain explicitly known, the control gain in this article is subject to uncertainty. Three different collective behaviors are explored, i.e., leaderless consensus, leader-following consensus, and formation control. For leaderless consensus, a sliding variable is constructed, based on which a novel continuous controller is designed such that the sliding surface is reached in finite-time and thus the state agreement of all agents is realized. For leader-following consensus, two different cases are investigated, i.e., the leader with constant velocity and with time-varying velocity. In both cases, sliding-mode based controllers are developed and corresponding stability conditions are established to ensure that the leader state is tracked by all followers. Finally, the theoretical results are applied to achieve formation control of nonholonomic mobile robots and corresponding experimental studies are conducted to demonstrate the effectiveness of the proposed controllers.

Fixed-time and prescribed-time consensus control can bring an explicit estimate of the settling time without dependence on initial conditions, which is important in providing control engineersa priorisystem information. This article aims at presenting a survey of recent trends and methodologies of fixed-time and prescribed-time consensus control in multiagent systems. First, some typical fixed-time consensus results are reviewed. Despite the advantage in deriving a fixed settling time bound, fixed-time consensus controllers usually result in a conservative estimate of the bound and a large magnitude of initial control input, which in turn show the necessity of designing prescribed-time consensus controllers. Second, characteristics and controller design of (practical, respectively) prescribed-time consensus are provided in detail. Particularly, representative time-varying function-based controllers are presented, by which (practical, respectively) consensus can be achieved in prescribed time. Third, applications of fixed-time and prescribed-time consensus control in mobile robots and smart grids are illustrated in case studies. Finally, several challenging issues in prescribed-time consensus control are discussed for future research.

This article deals with the problem of electromagnetic source localization (ESL). An evolutionary particle filter, which is first used to make a decision on the positions of electromagnetic sources, has two characteristics. One characteristic is that the number of particles can be significantly reduced while the other characteristic is that the particle diversity can be well improved. On the basis of the estimated positions of electromagnetic sources, the position and velocity of the virtual leader can be determined. Then, an event-based fixed-time consensus control approach is proposed such that the positions and velocities of robots reach consensus with the virtual leader over a fixed-time interval while saving resource consumption by reducing the communication frequencies and updating times of control inputs. Finally, simulation and experimental results show the effectiveness of the proposed decision and event-based fixed-time consensus control approach for ESL.

This article deals with the problem of leader-following consensus for multiple wheeled mobile robots. Under a directed graph, a distributed observer is proposed for each follower to estimate the leader state in a fixed time. Based on the observer and a constructed nonlinear manifold, a novel protocol is designed such that the estimated leader state is tracked in a fixed time. Moreover, a switching protocol together with a linear manifold is proposed to ensure that fixed-time leader-following consensus is realized for any initial conditions without causing singularity issues. In contrast to alternative fixed-time consensus protocols in some existing results, the protocol proposed in this article is designed by constructing the nonlinear or linear manifold, which builds a new framework for fixed-time leader-following consensus. Furthermore, the obtained upper bound of settling time is explicitly linked with a single parameter in the protocol, which facilitates the adjustment of the bound under different performance requirements. Finally, the proposed protocol is applied to formation control of wheeled mobile robots.

The paper presents a passivity-based control law for an antagonistic tendon-driven mechanism. It is proven, by using the passivity theorem, that the proposed control law is able to achieve two properties such as the passivity of interconnected subsystems when the external torque is applied and the global asymptotic stability during free motion when the external force is absent. The proposed controller is simple to be implemented for a complex tendon-driven mechanism because it requires only gravity compensation. In addition, it brings a robustness to the entire control system. And finally, the control strategy can be treated as one of the impedance control schemes so as to achieve the desired performance efficiently.

This paper deals with the problem of environmental monitoring by designing and analyzing a cooperative receding horizon temporal logic (CRH-TL) control approach for mobile robot networks. First, a radial basis function network is used to model the distribution of environmental attributes in the monitored environment. On the basis of the established environment model, the problem of environmental monitoring can be formulated as a dynamical optimization problem. Second, an acceptable node set is obtained by enforcing appropriate constraints from linear temporal logic (LTL) specifications on the task of environmental monitoring. Third, by designing a cooperative energy function and using the acceptable node set, the CRH-TL control approach is proposed to generate the movement trajectory of each robot, which satisfies the given LTL specifications while guiding mobile robot networks to trace the peaks of environmental attributes. Finally, the effectiveness of the proposed CRH-TL control approach is illustrated for the problem of environmental monitoring.

Photovoltaic(PV)-assisted charging station for electric vehicles(EVs) is an efficient way to balance the fluctuation produced by the renewable energy generation and EV charging loads. In the paper, a day-ahead optimal scheduling problem of a PV -assisted charging station(PVCS) in residential area is studied so as to reduce the operation cost of PVCS and prolong the life of EV batteries. Considering the dynamic capacity fading mode of EV batteries and the willingness of EV owners to participate in the vehicle-to-grid mode during the peak period, a dynamic nonlinear constrained optimization mathematical model is established and genetic algorithm(GA) is used to solve the optimal question. Then two different scenarios are simulated and compared, the simulation results show that the operating cost of the charging station and the life span of EV batteries are increased by 31.1 % and 34.8% separately when considering the cycle life span of EV batteries during the discharging process. These results validate the effectiveness and practicality of the proposed method in this paper.

This paper discusses the problem of local path planning in a static-obstacle environment by designing a PSO-based receding horizon control approach. In order to avoid obstacles, a virtual robot is first designed and moves along the boundary of obstacles. Then, in the framework of receding horizon control, a cost function is proposed where the virtual robot and the target position are integrated, which implies that mobile robots are controlled to keep a security distance and velocity consensus with virtual robots, and to move toward the target position. Next, the proposed cost function with constraints is processed by a particle swarm optimization (PSO) algorithm such that the PSO-based receding horizon control approach is developed. By solving the proposed cost function, a control sequence is obtained and then the first control input is used to enable the robot toward the target and avoid obstacles. Finally, the performance capabilities of the PSO-based receding horizon control approach are illustrated by simulation results.

This paper reviews the development history of simultaneous localization and mapping (SLAM) and concentrates on two mainstream methods: the filter-based method and the vision-based graph optimization method. FastSLAM and Real-Time Appearance-Based Mapping (RTAB-MAP) as two examples are adopted in the real experiments. The experiments are implemented on TurtleBot with Kinect in a small laboratory and a large circular corridor. The experimental results show that the error is small in the small laboratory, but in the highly unknown large scale environment, the loop closure detection is less effective and the error accumulation is obvious. The results show the accuracy and robustness of two algorithms need to be further improved when robots are in the large-scale unknown environments.

With the widespread use of smart appliances in the residential area, the load side of the microgrid is becoming more and more flexible. As we all know, the renewable energy generation is usually uncontrollable, so how to dispatch the controllable generation devices, storage system and flexible loads to guarantee the safe and economical operation of the microgrid is a very important problem to be solved. In this paper, a two-phase optimal strategy is proposed to deal with the optimal problem. In the first phase, the flexible loads are optimized to maximize the power consumption provided by renewable energy generation system. Then the day-ahead economic optimal dispatch problem is established to minimize the operating costs and environmental costs based on the optimized loads demand, and the Quantum behaved particle swarm optimization (QPSO) algorithm is adopted to solve it. Finally, a typical grid-connecting microgrid is taken as the study case, and the simulation results show that the reasonable use of the flexible loads can not only play the role of peak shifting and valley filling in electricity, but also increase the utilization rate of renewable energy, thereby reducing operating and environmental governance costs.

This paper deals with the problem of environmental monitoring by developing an event-triggered finite-time control scheme for mobile sensor networks. The proposed control scheme can be executed by each sensor node independently and consists of two parts: one part is a finite-time consensus algorithm while the other part is an event-triggered rule. The consensus algorithm is employed to enable the positions and velocities of sensor nodes to quickly track the position and velocity of a virtual leader in finite time. The event-triggered rule is used to reduce the updating frequency of controllers in order to save the computational resources of sensor nodes. Some stability conditions are derived for mobile sensor networks with the proposed control scheme under both a fixed communication topology and a switching communication topology. Finally, simulation results illustrate the effectiveness of the proposed control scheme for the problem of environmental monitoring.

How to improve consensus conditions for double-integrator multi-agent systems is investigated. First, a consensus algorithm is presented and the corresponding convergence condition is described. However, the given convergence condition is conservative such that consensus is still obtained when the parameters of the consensus algorithm violate the given convergence condition. Second, to cope with this issue, a less conservative convergence condition is proposed by using complex number and matrix properties. Finally, simulation results illustrate the effectiveness of the convergence condition through two examples.

A T-S fuzzy control scheme is designed for unicycle robots. First, a Lagrange method is used to model unicycle robots. Second, based on the established model, a T-S fuzzy approach is used to linearize the dynamics model of unicycle robots. Then, a parallel distributed compensator (PDC) is proposed, which is obtained by the linear matrix inequality approach. Third, stability criterions on unicycle robots with the proposed control scheme are expressed through using LMI tools. Finally, the effectiveness of the proposed control scheme is illustrated for unicycle robots.

This paper proposes an efficient wayfinding strategy for robot navigation in regionalized environments by designing a regionalized spatial knowledge model (RSK model) and a region-based wayfinding algorithm, i.e., a fine-to-coarse A* (FTC-A*) search algorithm. First, the RSK model, which imitates the representation of environments in the human brain, is presented to describe the search environments. The environments that are divided into regions are represented by a hierarchical nested structure where small regions are grouped together to form superordinate regions. Second, on the basis of the RSK model, an FTC-A* search algorithm is developed to plan the fine-to-coarse route. By making a fine planning to robot surroundings in vicinity, but a coarse planning to that at the distance, the FTC-A* algorithm can effectively reduce computational complexity, so as to enhance the efficiency of route search, and meanwhile makes robots to react quickly to user's commands, especially in large-scale environments. Finally, four exhaustive simulations and a physical experiment have been carried out to illustrate the feasibility and effectiveness of the proposed wayfinding strategy.

This paper deals with the problem of odor source localization by designing and analyzing a cooperative control framework (CCF) for the particle swarm optimization (PSO) algorithm. The CCF consists of three items: 1) a position coordination item; 2) a velocity coordination item; and 3) a movement direction coordination item. The position coordination item is used to coordinate relative positions between particles and to improve the exploration and exploitation capabilities of particles. The velocity coordination item enables the velocities of particles to reach consensus in order to realize orderly movement behaviors of particles. The movement direction coordination item guides particles to trace plumes and to locate odor sources. Stability of dynamic systems of particles with the proposed CCF is analyzed and the corresponding stability conditions are given such that the functions of three items in the CCF are realized. The orderly movement behaviors of particles under the CCF are also investigated based on benchmark functions. Finally, the effectiveness of the PSO algorithm with the proposed CCF is illustrated for the problem of odor source localization.

This paper is concerned with the event-triggered finite-time control scheme for unicycle robots. First, Lagrange method is used to model the unicycle robot at the roll and pitch axis. Second, on the basis of the established model, an event-triggered finite-time control scheme is proposed to balance the unicycle robot in finite time and to determine whether or not control input should be updated. The control input should be only updated when the triggering condition is violated. As a result, the switching energy of actor can be saved. Third, a stability criterion on unicycle robots with the proposed event-trigged finite-time control scheme is derived by using a Lyapunov method. Finally, the effectiveness of the event-triggered finite-time control scheme is illustrated for unicycle robots.

This paper deals with the problem of odor source localization by designing a collective decision-making mechanism based on Shannon's entropy and using two finite-time motion control algorithms for multiple mobile robots. Specifically, for the collective decision-making mechanism, a discrete grid map is first used to model the search environment. Then, the posteriori probability distribution for the position of the odor source on the discrete grid map is recursively updated by the detection events and non-detection events. Next, the Shannon's entropy for the probability distribution is employed to collectively make the decision on the movement direction of the robot group. For the motion control, the finite-time parallel motion control algorithm and the finite-time circular motion control algorithm are described. Moreover, two motion control algorithms are further extended in order to enable the robot group to avoid obstacles. Finally, the effective of the collective decision-making mechanism and two finite-time motion control algorithms is illustrated for the problem of odor source localization.

This paper presents a novel auction-based structure to multi-robot coalition formation problem. The structure, which is called multi-robot Coalition Structure Generation based on Credit Mechanism (CoSGCrM), contains a sub-optimal coalition member selection algorithm with an analysis of its soundness and completeness. A credit mechanism is introduced to reduce the complexity for the coalition leader in making a decision as well as to restrict the profit-oriented robot member in bidding for coalitions. Simulations are given to compare with first-price auction algorithm and the results show the viability of the proposed structure in both simple and complex tasks environments.

This paper deals with the problem of odor source localization by designing and analyzing a decision-control system (DCS) for a group of robots. In the decision level, concentration magnitude information and wind information detected by robots are used to predict a probable position of the odor source. Specifically, the idea of particle swarm optimization is introduced to give a probable position of the odor source in terms of concentration magnitude information. Moreover, an observation model of the position of the odor source is built according to wind information, and a Kalman filter is used to estimate the position of the odor source, which is combined with the position obtained by using concentration magnitude information in order to make a decision on the position of the odor source. In the control level, two types of the finite-time motion control algorithms are designed; one is a finite-time parallel motion control algorithm, while the other is a finite-time circular motion control algorithm. Precisely, a nonlinear finite-time consensus algorithm is first proposed, and a Lyapunov approach is used to analyze the finite-time convergence of the proposed consensus algorithm. Then, on the basis of the proposed finite-time consensus algorithm, a finite-time parallel motion control algorithm, which can control the group of robots to trace the plume and move toward the probable position of odor source, is derived. Next, a finite-time circular motion control algorithm, which can enable the robot group to circle the probable position of the odor source in order to search for odor clues, is also developed. Finally, the performance capabilities of the proposed DCS are illustrated through the problem of odor source localization.

This paper deals with a class of optimization problems by designing and analyzing a finite-time particle swarm optimization (FPSO) algorithm. Two versions of the FPSO algorithm, which consist of a continuous-time FPSO algorithm and a discrete-time FPSO algorithm, are proposed. Firstly, the continuous-time FPSO algorithm is derived from the continuous model of the particle swarm optimization (PSO) algorithm by introducing a nonlinear damping item that can enable the continuous-time FPSO algorithm to converge within a finite-time interval and a parameter that can enhance the exploration capability of the continuous-time FPSO algorithm. Secondly, the corresponding discrete-time version of the FPSO algorithm is proposed by employing the same discretization scheme as the generalized particle swarm optimization (GPSO) such that the exploiting capability of the discrete-time FPSO algorithm is improved. Thirdly, a Lyapunov approach is used to analyze the finite-time convergence of the continuous-time FPSO algorithm and the stability region of the discrete-time FPSO algorithm is also given. Finally, the performance capabilities of the proposed discrete-time FPSO algorithm are illustrated by using three wellknown benchmark functions (global minimum surrounded by multiple minima): Griewank, Rastrigin, and Ackley. In terms of numerical simulation results, the proposed continuous-time FPSO algorithm is used to deal with the problem of odor source localization by coordinating a group of robots.

This paper deals with the problem of odor source localization by using multi-robot systems. A distributed architecture, which consists of two layers: artificial intelligence layer and control layer, is proposed. Firstly, in the artificial intelligence layer, evolutionary algorithms, which can tackle information from other robots via communication networks, are employed. By using these evolutionary algorithms, the next state of a robot can be derived. Secondly, in the control layer, a consensus algorithm is used to control the robot to complete state transition from the current state to the new state derived. Finally, odor source localization problems are used to illustrate the effectiveness of the distributed architecture with two layers.