Numerous path-planning studies have been conducted in past decades due to the challenges of obtaining optimal solutions. This paper reviews multi-robot path-planning approaches and decision-making strategies and presents the path-planning algorithms for various types of robots, including aerial, ground, and underwater robots. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. The decision-making strategies mainly consist of centralized and decentralized approaches. The trend of the decision-making system is to move towards a decentralized planner. Finally, the new challenge in multi-robot path planning is proposed as fault tolerance, which is important for real-time operations.

Predicting the trajectory of flying objects with spin is a challenge but an essential task in many fields, especially in military and sports. Robots playing ping-pong is a very good platform to validate the trajectory prediction method. Various vision systems have been proposed, but only position information was used in most cases, which limits their capability to predict the trajectory of the spinning ball. Based on the fact that a spinning ball's motion can be separated into translation movement and spinning with respect to the ball's center, this paper proposes a novel vision system that can provide both the position and the spin information of a flying ball in a real-time mode with high accuracy. With a frame difference-based recognition method, the natural brand of a ball can be recognized under normal illumination conditions. Then the 3-D pose of the ball can be restored in ball coordinates. With the observation and analysis that the axis and angular speed of spin do not change during flying, the spin state can be estimated using a weighted-random sample consensus-based plane fitting method. Combining both position and spin information in a force-based dynamic model, accurate trajectory prediction can be achieved via an extended Kalman filter. Experimental results show the effectiveness and precision of the proposed method.

Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals’ awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots in indoor environments, which are popular applications of civilian robotics. The navigation approaches are classified by perception type, including vision-based, remote sensing, tactile sensor, olfactory, sound-based, inertial, and multimodal navigation. The trend of state-of-art techniques is moving towards multimodal navigation to combine several approaches. The challenges in indoor navigation focus on precise localization and dynamic and complex environments with moving objects and people.

For ping-pong playing robots, observing a ball and predicting a ball's trajectory accurately in real-time is essential. However, most existing vision systems can only provide ball's position observation, and do not take into consideration the spin of the ball, which is very important in competitions. This paper proposes a way to observe and estimate ball's spin in real-time, and achieve an accurate prediction. Based on the fact that a spinning ball's motion can be separated into global movement and spinning respect to its center, we construct an integrated vision system to observe the two motions separately. With a pan-tilt vision system, the spinning motion is observed through recognizing the position of the brand on the ball and restoring the 3D pose of the ball. Then the spin state is estimated with the method of plane fitting on current and historical observations. With both position and spin information, accurate state estimation and trajectory prediction are realized via Extended Kalman Filter(EKF). Experimental results show the effectiveness and accuracy of the proposed method.