论文标题
使用动态因素图的批次和增量运动动力运动计划
Batch and Incremental Kinodynamic Motion Planning using Dynamic Factor Graphs
论文作者
论文摘要
本文提出了一个动力学运动计划者，能够考虑到完整的机器人动力学，并利用重力，惯性和动力来减少努力，从而产生节能动作。鉴于机器人的特定目标状态，我们使用因子图和数值优化来解决最佳轨迹，该轨迹不仅符合避免碰撞的要求，还符合所有运动和动态约束，例如速度，加速度，加速度和扭矩限制。通过在因子图中利用稀疏性，我们可以有效地解决动力动力运动计划问题，与现有的最佳控制方法相提并论，并使用增量消除技术来实现更快的重新启动。
This paper presents a kinodynamic motion planner that is able to produce energy efficient motions by taking the full robot dynamics into account, and making use of gravity, inertia, and momentum to reduce the effort. Given a specific goal state for the robot, we use factor graphs and numerical optimization to solve for an optimal trajectory, which meets not only the requirements of collision avoidance, but also all kinematic and dynamic constraints, such as velocity, acceleration and torque limits. By exploiting the sparsity in factor graphs, we can solve a kinodynamic motion planning problem efficiently, on par with existing optimal control methods, and use incremental elimination techniques to achieve an order of magnitude faster replanning.
