A key problem in the formation control of homogeneous multiagent systems is the collision-free convergence of the agent positions into a desired formation. It is a typical NP-hard problem by considering the problem as optimizing the assignment of multiple destinations to the same number of agents deployed in an open space. It becomes even harder if the collision avoidance is required during the motion of agents, and thus, a suboptimal but efficient solution is adequate. The traditional methods make it by accurate preplanning of the motion trajectory of each single agent, or simply letting them reach an equilibrium as a tradeoff between the collision avoidance and the desired formation. In this article, a distributed control algorithm embedded with an assignment switch scheme is proposed to guarantee that the asymptotic convergence to the desired formation is achieved with no collisions between agents. By the proposed algorithm, the agents keep moving in straight lines toward their respective destinations until they are going to collide if they do not stop, at which moment the agents will communicate their information locally to switch their destination assignments so that they will continue to move in different directions and avoid potential collisions. Distributed control rules are also defined to confine the motion space of each agent for collision avoidance. It has been rigorously proven that the positions of all agents converge to the desired formation with no collision under random initial deployment. In addition, a detailed parameter design procedure is provided for both setting and controlling of the formation. Finally, Monte Carlo simulations and actual experiments in the outdoor environment are implemented and the results verify the effectiveness of the proposed algorithm.

This paper studies the problem of generating cooperative feasible paths for formation rendezvous of unmanned aerial vehicles (UAVs). Cooperative path-planning for multi-UAV formation rendezvous is mostly a complicated multi-objective optimization problem with many coupled constraints. In order to satisfy the kinematic constraints, i.e., the maximum curvature constraint and the requirement of continuous curvature of the UAV path, the Pythagorean hodograph (PH) curve is adopted as the parameterized path because of its curvature continuity and rational intrinsic properties. Inspired by the co-evolutionary theory, a distributed cooperative particle swarm optimization (DCPSO) algorithm with an elite keeping strategy is proposed to generate a flyable and safe path for each UAV. This proposed algorithm can meet the kinematic constraints of UAVs and the cooperation requirements among UAVs. Meanwhile, the optimal or sub-optimal paths can be obtained. Finally, numerical simulations in 2-D and 3-D environments are conducted to demonstrate the feasibility and stability of the proposed algorithm. Simulation results show that the paths generated by the proposed DCPSO can not only meet the kinematic constraints of UAVs and safety requirements, but also achieve the simultaneous arrival and collision avoidance between UAVs for formation rendezvous. Compared with the cooperative co-evolutionary genetic algorithm (CCGA), the proposed DCPSO has better stability and a higher searching success rate.

In this study, we address a coupled task allocation and path planning problem for a multi-unmanned aerial vehicle (UAV) reconnaissance/attack. Both coupled task allocation and path planning problem are addressed. We propose a task allocation algorithm for maximizing system utility and a path planning algorithm for simultaneous arrival. Moreover, we consider the targets resource requirement and UAVs resource constraints based on a contract net protocol for task allocation. Benefits of destroying the target and costs of UAV attacking target are considered in the objective function. To address the multi-UAV path planning problem, a combination of cooperative particle swarm optimization algorithm, coordination variables, and coordination functions is proposed. UAVs kinematics constraint is considered for the path planning method. Compared with a polynomial time coalition formation algorithm (PTCFA), simulation results show that the proposed algorithm improves the average performance by at least $8%$ with simultaneous arrival using the path planning method.

This paper describes our work on a small, hand-launched, solar-powered unmanned aerial vehicle (UAV) suitable for low temperatures and high altitudes, which has the perpetual flight potential for conservation missions for rare animals in the plateau area in winter. Firstly, the conceptual design method of a small, solar-powered UAV based on energy balance is proposed, which is suitable for flight in high-altitude and low-temperature area. The solar irradiance model, which can reflect the geographical location and time, was used. Based on the low-temperature discharge test of the battery, a battery weight model considering the influence of low temperature on the battery performance was proposed. Secondly, this paper introduces the detailed design of solar UAV for plateau area, including layout design, structure design, load, and avionics. To increase the proportion of solar cells covered, the ailerons were removed and a rudder was used to control both roll and yaw. Then, the dynamics model of an aileron-free layout UAV was developed, and the differences in maneuverability and stability of aileron-free UAV in plateau and plain areas were analyzed. The control law and trajectory tracking control law were designed for the aileron-free UAV. Finally, the flight test was conducted in Qiangtang, Tibet, at an altitude of 4500 m, China’s first solar-powered UAV to take off and land above 4500 m on the plateau in winter (−30 °C). The test data showed the success of the scheme, validated the conceptual design method and the success of the control system for aileron-free UAV, and analyzed the feasibility of perpetual flight carrying different loads according to the flight energy consumption data.

A hierarchical mission planning method was proposed to solve a simultaneous attack mission planning problem for multi-unmanned aerial vehicles (UAVs). The method consisted of three phases aiming to decouple and solve the mission planning problem. In the first phase, the Pythagorean hodograph (PH) curve was used in the path estimation process for each UAV, which also served as the input for the task allocation process. In the second phase, a task allocation algorithm based on a negotiation mechanism was proposed to assign the targets. Considering the resource requirement, time-dependent value of targets and resource consumption of UAVs, the proposed task allocation algorithm can generate a feasible allocation strategy and get the maximum system utility. In the last phase, a path planning method was proposed to generate a simultaneous arrival PH path for each UAV considering UAV’s kinematic constraint and collision avoidance with obstacles. The comparison simulations showed that the path estimation process using the PH curve and the proposed task allocation algorithm improved the system utility, and the hierarchical mission planning method has potential in a real mission.

The coupled task allocation and path planning problem for heterogeneous multiple unmanned aerial vehicles performing a search and attack mission involving obstacles and no-fly zones are addressed. The importance of the target is measured using a time-dependent value. A task allocation algorithm is proposed to obtain the maximum system utility. In the system utility function, the reward of the target, path lengths of unmanned aerial vehicles, and number of unmanned aerial vehicles to perform a simultaneous attack are considered. The path length of the unmanned aerial vehicles based on the Pythagorean hodograph curve is calculated, and it serves as the input for the task allocation problem. A resource management method for unmanned aerial vehicles is used, so that the resource consumption of the unmanned aerial vehicles can be balanced. To satisfy the requirement of simultaneous attacks and the unmanned aerial vehicle kinematic constraints in an environment involving obstacles and no-fly zones, a distributed cooperative particle swarm optimization algorithm is developed to generate flyable and safe Pythagorean hodograph curve trajectories for unmanned aerial vehicles to achieve simultaneous arrival. Monte Carlo simulations are conducted to demonstrate the performance of the proposed task allocation and path planning method.