Martinez Rocamora Bernardo, Pereira Guilherme A S
Department of Mechanical and Aerospace Engineering, Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV 26506, USA.
Sensors (Basel). 2022 Jun 24;22(13):4770. doi: 10.3390/s22134770.
This paper presents a parallel motion planner for mobile robots and autonomous vehicles based on lattices created in the sensor space of planar range finders. The planner is able to compute paths in a few milliseconds, thus allowing obstacle avoidance in real time. The proposed sensor-space lattice (SSLAT) motion planner uses a lattice to tessellate the area covered by the sensor and to rapidly compute collision-free paths in the robot surroundings by optimizing a cost function. The cost function guides the vehicle to follow a vector field, which encodes the desired vehicle path. We evaluated our method in challenging cluttered static environments, such as warehouses and forests, and in the presence of moving obstacles, both in simulations and real experiments. In these experiments, we show that our algorithm performs collision checking and path planning faster than baseline methods. Since the method can have sequential or parallel implementations, we also compare the two versions of SSLAT and show that the run time for its parallel implementation, which is independent of the number and shape of the obstacles found in the environment, provides a speedup greater than 25.
本文提出了一种基于平面测距仪传感器空间中创建的格网的移动机器人和自动驾驶车辆并行运动规划器。该规划器能够在几毫秒内计算路径，从而实现实时避障。所提出的传感器空间格网（SSLAT）运动规划器使用格网对传感器覆盖的区域进行细分，并通过优化成本函数在机器人周围快速计算无碰撞路径。成本函数引导车辆跟随一个向量场，该向量场编码了期望的车辆路径。我们在具有挑战性的杂乱静态环境（如仓库和森林）以及存在移动障碍物的情况下，通过模拟和实际实验对我们的方法进行了评估。在这些实验中，我们表明我们的算法比基线方法执行碰撞检查和路径规划的速度更快。由于该方法可以有顺序或并行实现，我们还比较了SSLAT的两个版本，并表明其并行实现的运行时间与环境中发现的障碍物的数量和形状无关，加速比大于25。
