I-Planner: Intention-aware motion planning using learning-based human motion prediction
I-Planner: Intention-aware motion planning using learning-based human motion prediction
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DOI：
10.1177/0278364918812981
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发表时间：
2019-01-01
影响因子：
9.2
通讯作者：
Manocha, Dinesh
中科院分区：
文献类型：
--
作者：
Park, Jae Sung;Park, Chonhyon;Manocha, Dinesh
我们提出了一种运动规划算法来计算高自由度（高自由度）机器人与人类在共享工作空间中进行交互的无碰撞和平滑的轨迹。我们的方法使用离线学习的人类行动沿着与时间的一致性来预测人类的行动。我们的意图感知在线规划算法使用学习的数据库根据预测的动作计算可靠的轨迹。我们表示预测的人体运动使用高斯分布和计算安全的运动规划碰撞概率的严格上限。我们还描述了新的技术，以占人体运动预测中的噪声。我们强调了我们的规划算法在复杂的模拟场景和现实世界的基准与7自由度机器人手臂在一个工作空间与人类执行复杂的任务。我们证明了我们的意图感知规划的好处，在这种不确定的环境中计算安全的轨迹。
We present a motion planning algorithm to compute collision-free and smooth trajectories for high-degree-of-freedom (high-DOF) robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe novel techniques to account for noise in human motion prediction. We highlight the performance of our planning algorithm in complex simulated scenarios and real-world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.