未知环境下基于控制障碍函数的无人车轨迹规划

Control Barrier Functions-based Trajectory Planning for Unmanned Ground Vehicles in Unknown Environment

无人车凭借其适应性强、成本低的优点,成为代替人类前往高风险、高污染区域执行任务的一种切实可行的方案。轨迹规划作为无人车任务系统的关键技术,其目的是根据任务目标制定满足约束条件的运动轨迹。然而,目前的导航控制器主要依赖于预先获取的地图和先验知识,对未知环境的应对能力有所欠缺,无法适应复杂多变的任务环境。为此,提出了一种基于控制障碍函数的轨迹规划方法,用于解决无人车在未知环境下的自主避障、轨迹规划问题。该方法前端利用激光雷达感知环境深度信息,后端采用最小二乘支持向量机拟合障碍物边界,以估计控制障碍函数,并对负样本错分类进行补偿,最终通过求解二次规划得到安全控制指令。针对无人车可能陷入停滞状态的死锁问题,提出一种分段式的检测与消除方法来避免。数值仿真实验和半实物实验结果表明,所提方法在多种障碍物环境下具备较好的避障避撞和轨迹规划能力,在路径长度等方面优于传统的控制方法。

The ummaned ground vehicles(UGVs)have become a practical alternative to humans in performing tasks in high-risk,high-pollution are as due to their strong adaptability and low cost.As a key technology of mission system for UGV,the trajectory planning is to develop a motion trajectory that meets the constraints based on mission objectives.However,the current navigation controllers rely primarily on the pre-acquired maps and prior knowledge,and are lacking in the ability to cope with unknown environments,making UGVs unable to adapt to complex and changing task environments.Therefore,a trajectory planning method based on the control barrier function(CBF)is proposed to solve the autonomous obstacle avoidance and trajectory planning problems of UGVs in unknown environments.The front end of the method utilizes a laser radar to perceive environmental depth information,while its back end uses a least squares support vector machine(LSSVM)to fit the boundary of obstacles to estimate the control barrier function and compensate for negative sample misclassification.Finally,the safety control instruction is obtained by solving a quadratic programming problem.A segmented detection and elimination method is proposed to address the issue of deadlocks that may occur when UGVs come to a standstill.The results of numerical simulation experiments and semi-physical experiments show that the proposed method has good obstacle avoidance and trajectory planning capabilities in various obstacle environments,and is superior to traditional control methods in terms of path length and other aspects.