Mobile robots, including ground robots, underwater robots, and unmanned aerial vehicles, play an increasingly important role in people’s work and lives. Path planning and obstacle avoidance are the core technologies for achieving autonomy in mobile robots, and they will determine the application prospects of mobile robots. This paper introduces path planning and obstacle avoidance methods for mobile robots to provide a reference for researchers in this field. In addition, it comprehensively summarizes the recent progress and breakthroughs of mobile robots in the field of path planning and discusses future directions worthy of research in this field. We focus on the path planning algorithm of a mobile robot. We divide the path planning methods of mobile robots into the following categories: graph-based search, heuristic intelligence, local obstacle avoidance, artificial intelligence, sampling-based, planner-based, constraint problem satisfaction-based, and other algorithms. In addition, we review a path planning algorithm for multi-robot systems and different robots. We describe the basic principles of each method and highlight the most relevant studies. We also provide an in-depth discussion and comparison of path planning algorithms. Finally, we propose potential research directions in this field that are worth studying in the future.

Brain-inspired navigation technologies combine environmental perception, spatial cognition, and target navigation to create a comprehensive navigation research system. Researchers have used various sensors to gather environmental data and enhance environmental perception using multimodal information fusion. In spatial cognition, a neural network model is used to simulate the navigation mechanism of the animal brain and to construct an environmental cognition map. However, existing models face challenges in achieving high navigation success rate and efficiency. In addition, the limited incorporation of navigation mechanisms borrowed from animal brains necessitates further exploration. On the basis of the brain-inspired navigation process, this paper launched a systematic study on brain-inspired environment perception, brain-inspired spatial cognition, and goal-based navigation in brain-inspired navigation, which provides a new classification of brain-inspired cognition and navigation techniques and a theoretical basis for subsequent experimental studies. In the future, brain-inspired navigation technology should learn from more perfect brain-inspired mechanisms to improve its generalization ability and be simultaneously applied to large-scale distributed intelligent body cluster navigation. The multidisciplinary nature of brain-inspired navigation technology presents challenges, and multidisciplinary scholars must cooperate to promote the development of this technology.

In unknown or complex environments, the security threats of multi-robot system come from various aspects. Security protection requires that robots can avoid collisions with each other while meeting obstacles to ensure overall stability and safety. This study proposes a predictable and highly secure multi-robot flocking obstacle avoidance algorithm based on an improved artificial potential field (MRF-IAPF). In the proposed MRF-IAPF algorithm, the robot detects an obstacle and calculates its inhibiting velocity based on the associated dynamic characteristics. The inhibiting velocity is then combined with the interaction velocity between robots and their neighbors, as well as the interaction velocity between robots and the target point to obtain the control inputs that can maintain the robot flocking state and implement dynamic obstacle avoidance, simultaneously. The control part adopts decentralized manner. When some robots are attacked or faulty, others are not affected. Thus, the proposed approach can dynamically adjust to increase the safety and stability of the system. Theoretical analysis and experimental results verify that the proposed obstacle avoidance algorithm can effectively avoid dynamic obstacles and maintain the state of a multi-robot flocking to avoid accidents in the multi-robot system.

Automatically detecting human mental workload to prevent mental diseases is highly important. With the development of information technology, remote detection of mental workload is expected. The development of artificial intelligence and Internet of Things technology will also enable the identification of mental workload remotely based on human physiological signals. In this paper, a method based on the spatial and time–frequency domains of electroencephalography (EEG) signals is proposed to improve the classification accuracy of mental workload. Moreover, a hybrid deep learning model is presented. First, the spatial domain features of different brain regions are proposed. Simultaneously, EEG time–frequency domain information is obtained based on wavelet transform. The spatial and time–frequency domain features are input into two types of deep learning models for mental workload classification. To validate the performance of the proposed method, the Simultaneous Task EEG Workload public database is used. Compared with the existing methods, the proposed approach shows higher classification accuracy. It provides a novel means of assessing mental workload.

Robots instead of humans work in unstructured environments, expanding the scope of human work. The interactions between humans and robots are indirect through operating terminals. The mental workloads of human increase with the lack of direct perception to the real scenes. Thus, mental workload assessment is important, which could effectively avoid serious accidents caused by mental overloading. In this paper, the operating object is a dual-arm robot. The classification of operator’s mental workload is studied by using the heart rate variability (HRV) signal. First, two kinds of electrocardiogram (ECG) signals are collected from six subjects who performed tasks or maintained a relaxed state. Then, HRV data is obtained from ECG signals and 20 kinds of HRV features are extracted. Last, six different classifications are used for mental workload classification. Using each subject’s HRV signal to train the model, the subject’s mental workload is classified. Average classification accuracy of 98.77% is obtained using the K-Nearest Neighbor (KNN) method. By using the HRV signal of five subjects for training and that of one subject for testing with the Gentle Boost (GB) method, the highest average classification accuracy (80.56%) is obtained. This study has implications for the analysis of HRV signals characteristic of mental workload in different subjects, which could improve operators’ well-being and safety in the human-robot interaction process.