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The purpose of this paper is to understand the recovery mechanism of poly(trimethylene terephthalate) (PTT) shape memory fabrics.

Tests were designed to study the effects of force, temperature and their combinations on the fabrics' crease recoveries. In the test a cantilever device and an ironing force which simulated people ironing their clothes were used, respectively.

Temperature was found to have little effect on the recovery of both the warp and filling of the fabrics. Crease recoveries did not improve significantly when the temperature was increased to above the polymer's glass transition. However, forces, applied in primarily compressive and tensile modes to simulate ironing and hand stroking actions, were found to be very effective in the fabrics' crease recoveries. Recoveries were 81‐87 per cent even when the applied force was very small, at 5 N/cm². When forces were applied at elevated temperatures, just below and above the polymer's glass transition, there were no significant improvements in crease recoveries. Therefore, force was the main factor in PTT shape memory fabrics' recovery mechanism for the fabrics to return to their initial shapes.

The results suggest that PTT shape memory fabric has excellent shape recoverability and easy care property and it has large application potentiality.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.