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, 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 presents the design and analysis of an omnidirectional reconfigurable wheeled robot capable of switching between omnidirectional and conventional wheeled mode. We have developed a new pneumatic locking mechanism of rollers for the mecanum wheel. In the mecanum mode, the robot can perform holonomic movements, and in the wheeled platform mode, it can overcome inclined surfaces and perform more energy-efficient movements. In addition, the locking device allows the robot to brake faster compared to other mecanum robots. Unlike other works describing the reconfigurable structure of the mecanum wheel, this work offers a new design characterized by the simplicity of the mechanism and does not require the location of active reconfiguration elements inside the wheel itself. The paper describes the design concept and presents the mechanism for locking rollers. The study evaluates the use of the developed robot in various scenarios, including movement on an inclined surface, sudden braking on a plane and an inclined surface, and also analyzes the energy efficiency of the resulting solution for some operating scenarios. The experiments carried out confirm that this mobile platform, when switching mode, is able to move on surfaces with a large angle of inclination and perform more effective deceleration on both flat and inclined surfaces.

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.

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 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.

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 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.