Path planning enables Unmanned Aerial Vehicles (UAVs) to generate safe and efficient trajectories toward mission goals, minimizing flight time and energy consumption, while cooperative collision avoidance ensures reliable operation of UAV swarms in dense and dynamic environments. Introducing these two functions together is crucial for enhancing both the autonomy and robustness of UAV systems. This paper presents a novel dynamic path planning and collision avoidance algorithm for multi-UAV systems, known as the Independent Proximal Policy Optimization with Cooperative Collision Avoidance (IPPO-CCA) algorithm. The proposed algorithm integrates Independent Proximal Policy Optimization (IPPO) with Optimal Reciprocal Collision Avoidance (ORCA) and Region-Guided Collision Avoidance (RGCA) to improve navigation efficiency and flight safety in complex environments. Using a shared policy network and a bidirectional gated recurrent unit model, IPPO-CCA enables each UAV to independently learn optimal action strategies, achieving collision-free flight paths and flexible route adjustments. Simulation results across various scenarios confirm that IPPO-CCA significantly improves the overall safety, adaptability, and efficiency of multi-UAV missions. In quantitative terms, IPPO-CCA outperforms MASAC-CCA and MADDPG-CCA in average final reward by 13.66% and 21.70%, respectively. The source code is available at https://github.com/Shihong-Yin/IPPO-CCA.

This paper investigates the fully distributed scaled consensus problem of switched multi-agent systems, in which the Multi-Agent System (MAS) involves both the switching characteristics of individual agent models and communication networks. Control inputs are subject to saturation constraints, and system states are affected by scaling properties. A stochastic switching law that integrates the advantages of Markov switching and dwell-time switching is employed. The difficulties caused by random switching of agent models and communication networks, unknown global information of the communication network, input saturation constraints, and scaling properties among individual states make the fully distributed scaled consensus problem difficult to be solved directly by existing approaches. Therefore, this paper designs a novel adaptive scaled consensus algorithm and a Lyapunov function, and introduces an algebraic Riccati equation. On this basis, the conditions are established under which the switched multi-agent system can achieve almost surely exponential scaled consensus in a fully distributed fashion. In particular, the obtained conditions admit feasible solutions and are very easy to solve. Finally, an unmanned aerial vehicle swarm is introduced to verify the effectiveness of the proposed control scheme.