In this work, we propose a collision-free source seeking control framework for unicycle robots traversing an unknown cluttered environment. In this framework, the obstacle avoidance is guided by the control barrier functions (CBF) embedded in quadratic programming and the source seeking control relies solely on the use of on-board sensors that measure signal strength of the source. To tackle the mixed relative degree of the CBF, we proposed three different CBF, namely the zeroing control barrier functions (ZCBF), exponential control barrier functions (ECBF), and reciprocal control barrier functions (RCBF) that can directly be integrated with our recent gradient-ascent source-seeking control law. We provide rigorous analysis of the three different methods and show the efficacy of the approaches in simulations using Matlab, as well as, using a realistic dynamic environment with moving obstacles in Gazebo/ROS.
翻译:在这项工作中,我们建议为穿越未知的杂乱环境的单周期机器人建立一个无碰撞源控制框架,在这一框架内,障碍的避免以四边程序内嵌入的控制屏障功能(CBF)为指导,寻求控制的源完全依赖于使用测量源强度的机载传感器,以测量源的信号强度。为解决CBF的相对程度混杂问题,我们建议三个不同的CBF,即零控制屏障功能(ZCBF)、指数控制屏障功能(ECBF)和对等控制屏障功能(RCBF),这些功能可以直接与我们最近的梯度高度源寻求控制法相结合。我们对三种不同的方法进行严格分析,并展示使用Matlab模拟方法的功效,以及在Gazebo/ROS使用具有移动障碍的现实动态环境。