In the task planning of heterogeneous unmanned aerial vehicle formations, problems such as dynamic topological instability and sparse Pareto front exist, which affect the robustness of the planning. To address this, this paper proposes a cooperative task planning method based on multi-objective dolphin echolocation optimization driving. Firstly, a differentiated dynamic model of heterogeneous unmanned aerial vehicles covering different configurations such as rotors and fixed wings is constructed, and a dynamic communication topology model is established based on time-varying graph theory to quantify transmission delay and link stability. Then, a multi-objective optimization model is designed with task completion, energy balance, and time cost as the core, Bayesian networks are introduced to construct a dynamic threat field, and risk assessment and real-time response are achieved in complex environments. Based on this, a multi-objective dolphin echo optimization algorithm is adopted to solve the model, and its echo beam focusing search and adaptive weight allocation mechanism are utilized to effectively improve the convergence and distribution of the Pareto solution set. Finally, a “decision execution” hierarchical collaborative control architecture is constructed, utilizing the decision layer to output a global planning scheme and the execution layer to achieve rolling optimization and precise tracking of instructions through distributed model predictive control. The simulation test results show that this method can maintain high task completion, energy balance, and communication stability in different formation sizes and complex environments significantly better than traditional algorithms. When the formation size is between 20 and 60 sorties, the hypervolume (HV) index of this method is superior to that of the comparison method. In cases of sudden obstacles and complex electromagnetic interference scenarios, the average energy consumption of a single unmanned aerial vehicle after applying this method is maintained at 150–250 Wh, and the transmission delay is stable at 50–200 ms. The experimental results verify that this method has good planning robustness and collaborative real-time performance.

Small objects in video streams occupy a small proportion in the image; the texture and shape information they carry is limited, making it difficult to continuously track and identify. To solve this problem, a method for continuous tracking and recognition of small objects in the video stream based on YOLO and spatio-temporal context memory network is proposed. A backbone network based on the improved YOLOv8 model is introduced, and the multi-scale visual features of small objects are extracted at different levels of the video stream using the wavelet pooling module. A mixed attention module enhances the feature response in the spatially significant pixel regions, generating weighted multi-scale visual features. The neck network processes these weighted features through a spatio-temporal context memory network to extract multi-scale spatio-temporal features. Then, a bidirectional feature pyramid module fuses these multi-scale spatio-temporal features. The head network processes the fused features to output continuous recognition results for small objects. Experiments show that the proposed method successfully extracts the spatiotemporal features of small objects from video stream data sets dominated by small objects. Under different conditions of small object occlusion rates, this method achieves a success rate of continuous tracking and recognition of small objects over 0.93.

This paper presents a method for determining weight coefficients in multi-objective UAV task assignment under a competitive data-collection setting, where two UAV companies independently visit each other’s sensor nodes. In this setting, each company expresses operational intent through weights for data value, distance utility, and flight safety. Our own weights are prescribed by higher-level planning, whereas the competitor’s latent weights must be estimated from observed visit assignments. To estimate these weights, we apply Approximate Bayesian-Inspired Inference, which also provides a measure of the associated inference uncertainty. Subsequently, a purpose-built fuzzy rule base translates the inferred weights into response weights, while the fuzziness of this mapping is quantified as a second source of uncertainty. From these two uncertainty measures, an adaptive coefficient is derived that blends the planning and response weights into the final task assignment. Under controlled synthetic settings, numerical experiments demonstrate that the inference procedure outperforms direct loss-minimization baselines in both accuracy and speed, and that the adaptive fusion effectively balances planning adherence with responsiveness to competitor behavior.

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety.