With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a compl...With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an off-road environment because of its uncertainty and diversity.A single type of sensor finds it hard to satisfy the needs of obstacle detection because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop multi-sensor fusion and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the off-road environments and providing users with a guideline for selecting sensors based on their performance requirements and application environments.State-of-the-art multi-sensor fusion methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.展开更多
In this article we specify an individual-based foraging swarm （i.e., group of agents） model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual （i） is determine...In this article we specify an individual-based foraging swarm （i.e., group of agents） model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual （i） is determined by three factors： i） attraction to the local object position （x^-io＋） which is decided by the local information about the individuals＇ position that individual i can find; ii） repulsion from the other individuals on short distances; and iii） attraction to the global object position （xgoal） or repulsion from the obstacles in the environment, The emergent behavior of the swarm motion is the result of a balance between inter-individual interaction and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm based on Lyapunov stability theory. The simulations show that the swarm can converge to goal regions and diverge from obstacle regions of the environment while maintaining cohesive.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.61603303,61803309,and 61703343)the Natural Science Foundation of Shaanxi Province,China(No.2018JQ6070)+1 种基金the China Postdoctoral Science Foundation(No.2018M633574)the Fundamental Research Funds for the Central Universities,China(Nos.3102019ZDHKY02 and3102018JCC003)。
文摘With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an off-road environment because of its uncertainty and diversity.A single type of sensor finds it hard to satisfy the needs of obstacle detection because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop multi-sensor fusion and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the off-road environments and providing users with a guideline for selecting sensors based on their performance requirements and application environments.State-of-the-art multi-sensor fusion methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.
基金This work was supported by the National Natural Science Foundation of China (No. 60574088).
文摘In this article we specify an individual-based foraging swarm （i.e., group of agents） model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual （i） is determined by three factors： i） attraction to the local object position （x^-io＋） which is decided by the local information about the individuals＇ position that individual i can find; ii） repulsion from the other individuals on short distances; and iii） attraction to the global object position （xgoal） or repulsion from the obstacles in the environment, The emergent behavior of the swarm motion is the result of a balance between inter-individual interaction and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm based on Lyapunov stability theory. The simulations show that the swarm can converge to goal regions and diverge from obstacle regions of the environment while maintaining cohesive.
