Active object detection (AOD), one of the greatest challenges in the robotics field, is the main focus of this article. Most current AOD methods are developed by reinforcement learning (RL) algorithms while they can be further improved in the aspects of training time, training efficiency, model performance, and model prediction. Therefore, different from the existing works, we propose an AOD method based on behavior cloning trained by automatically generated data. We transform the AOD task into an action classification problem to not only shorten the training time but also improve the training efficiency and model performance. As there is no available expert data for training the presented classification-based AOD model, we design an autonomous method of data generation to avoid the large amounts of manual annotations. We introduce a multiinput network for better obstacle avoidance and AOD performance, where the depth image is added to help the robot to perceive distance information of environments and objects. Moreover, we develop a revision method for model prediction to reduce the accumulation of compounding error, which improves the successful rate of the long path AOD tasks effectively. We extensively evaluate our method on an AOD dataset by the comparable experiments and the ablation study, proving that our approach outperforms other methods in AOD performance and efficiency. In addition, the AOD experiments in the real-world scenario with a TIAGo robot indicate the validity of our method.

This paper focuses on the problem of active object detection (AOD). AOD is important for service robots to complete tasks in the family environment, and leads robots to approach the target object by taking appropriate moving actions. Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy. Therefore, an AOD model based on a deep Q-learning network (DQN) with a novel training algorithm is proposed in this paper. The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction, and a multilayer perceptron. In contrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy. In addition, a method of generating the end state is presented to judge when to stop the AOD task during the training process. Sufficient comparison experiments and ablation studies are performed based on an AOD dataset, proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.

The proven efficacy of safety strategies based on 2-D laser rangefinder (LRF) strongly stimulates their application to mobile robots operating in the home environment. However, it remains a challenge for the robot to avoid collisions with all obstacles in the environment. Since LRF can only scan a horizontal slice of the world, some objects cannot be fully observed, such as tables and chairs. In this article, an effective solution based on laser-visual fusion is presented to enhance the safety of the robot. First, a vision sensor is adopted to help detect obstacles that are not fully visible to LRF. Then we propose a method to convert the depth information of the visual image into 2-Dpseudo-laser datarepresentation. With this representation, a strategy for 2-D mapping is developed. On this basis, a novel map fusion algorithm is proposed to generate an improved grid map that amends the incorrect representation of obstacles on the traditional 2-D grid map. We further investigate a robot autonomous navigation strategy that considers LRF data and pseudo-laser data to avoid all obstacles. Experimental results show that the improved grid map together with the presented navigation strategy allows the robot not only to plan a “real” collision-free path, but also to navigate safely in both static and dynamic scenarios, and the proposed strategies can significantly enhance the performance of robot navigation in terms of safety, reliability and robustness.