越野环境下的无人车能耗最优路径规划方法

As the demand for unmanned ground vehicle (UGV) to perform tasks in rough terrain environment is increasing, it becomes more important to reduce energy consumption in order to perform more tasks more efficiently with limit energy supply. Taking into account the demand for reducing energy consumption and improving the efficiency of path planning, this research proposes an energy-efficient path planning method based on heuristic search algorithm. The dynamic model of UGV is established based on the relationship between velocity, friction resistance, gravity, the output torque of motor and the rotation velocity of wheel. The power model is designed based on the dynamic model of UGV. The sample-based model prediction algorithm is used to generate the optimal trajectory cluster based on the kinematics model of UGV. The valid trajectory in the cluster is used to estimate the cost of the trajectory according to distance cost consumption and energy consumption, thereby generating an offline trajectory database. By integrating the energy consumption into the heuristic search algorithm's evaluation function, an energy-efficient path planning method is proposed based on heuristic search algorithm. According to the connection relationship of nodes and environment map, online planning process is carried out to find the optimal energy consumption path.

随着对无人车在越野环境执行任务的需求增加，为了使无人车在能量补给不足的情况下更高效地执行更多的任务，降低运动过程中的能耗变得尤为重要。考虑到无人车对降低能耗和路径规划效率两方面的需求，本项目提出基于启发式搜索的能耗最优路径规划方法。分析无人车在运动过程中的运动速度、地面阻力及重力与驱动电机输出扭矩、车轮转动速度之间的数学关联，构建无人车的动力学模型，并据此建立无人车在运动过程中的能耗模型。基于无人车的运动学模型，采用基于采样的模型预测算法生成优化轨迹簇，对轨迹簇中的有效轨迹按照距离成本消耗和能耗模型评估轨迹通行消耗成本，从而生成离线的轨迹库。通过将轨迹能耗融入启发式搜索算法的估价函数，提出基于启发式搜索的能耗最优路径规划方法，根据轨迹库中的节点连接关系和环境地图进行在线规划，以获得能耗最优的路径。

随着对无人车在越野环境执行任务的需求增加，为了使无人车在能量补给不足的情况下更高效地执行更多的任务，降低运动过程中的能耗变得尤为重要。考虑到无人车对降低能耗和路径规划效率两方面的需求，本项目提出了基于启发式搜索的能耗最优路径规划方法。重点开展了基于无人车动力学分析的能耗模型构建、基于运动学约束和能耗模型的优化轨迹簇生成及基于启发式搜索的能耗最优路径规划方法研究。在本项目实验过程中，分别构建了大量离线栅格地图和虚拟仿真场景对所提出的规划算法进行测试，测试结果表明所生成的路径总能耗相比距离最优路径的总能耗平均降低约28%，从而验证了算法的有效性，为解决能耗约束下的最优路径规划问题提出了新思路和新方法。