To improve image object detection and tracking, researchers have been exploring methods to enhance the stability and precision of optoelectronic platforms’ line of sight (LOS). The innovation of stability mechanisms is the key driver of this breakthrough. This study presents a composite stability control system for reflective optoelectronic platforms using the integral composite stability principle. A platform kinematic model was established based on multi-body kinematic theory, and a composite stable control strategy was designed. The strategy includes coarse stability design and fine stability design based on residual error feed-forward correction. The performance of the control strategy was analyzed in terms of dynamics, current loop control effects, and loop structure. The proposed control strategy was simulated and experimentally verified for fixed-frequency angular velocity disturbance and translational disturbance. The stability accuracy index of the system was significantly improved after compensation, with improvement of more than 75 times for fixed-frequency angular velocity disturbance and more than 37% for translational disturbance. Comparative experimental results with traditional stable methods show that the proposed composite stable control strategy can significantly improve the system stability, with stability accuracy index improvement of one to two orders of magnitude in micro-radian units compared to traditional algorithms.

Cooperative autonomous exploration is a challenging task for multi-robot systems, which can cover larger areas in a shorter time or path length. Using multiple mobile robots for cooperative exploration of unknown environments can be more efficient than a single robot, but there are also many difficulties in multi-robot cooperative autonomous exploration. The key to successful multi-robot cooperative autonomous exploration is effective coordination between the robots. This paper designs a multi-robot cooperative autonomous exploration strategy for exploration tasks. Additionally, considering the fact that mobile robots are inevitably subject to failure in harsh conditions, we propose a self-healing cooperative autonomous exploration method that can recover from robot failures.

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Purpose Robots equipped with LiDAR sensors can continuously perform efficient actions for mapping tasks to gradually build maps. However, with the complexity and scale of the environment increasing, the computation cost is extremely steep. This study aims to propose a hybrid autonomous exploration method that makes full use of LiDAR data, shortens the computation time in the decision-making process and improves efficiency. The experiment proves that this method is feasible. Design/methodology/approach This study improves the mapping update module and proposes a full-mapping approach that fully exploits the LiDAR data. Under the same hardware configuration conditions, the scope of the mapping is expanded, and the information obtained is increased. In addition, a decision-making module based on reinforcement learning method is proposed, which can select the optimal or near-optimal perceptual action by the learned policy. The decision-making module can shorten the computation time of the decision-making process and improve the efficiency of decision-making. Findings The result shows that the hybrid autonomous exploration method offers good performance, which combines the learn-based policy with traditional frontier-based policy. Originality/value This study proposes a hybrid autonomous exploration method, which combines the learn-based policy with traditional frontier-based policy. Extensive experiment including real robots is conducted to evaluate the performance of the approach and proves that this method is feasible.

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