论文标题
环境感知的互动运动原始基底物，用于杂乱的物体到达
Environment-aware Interactive Movement Primitives for Object Reaching in Clutter
论文作者
论文摘要
在文献上开发的大多数运动计划策略用于到达混乱中的一个物体，都应用于二维（2-d）空间，其中环境的状态空间在一个方向上受到约束。研究了在3-D混乱的空间中达到目标的研究更少，而当应用于复杂情况时，它们的性能有限。在这项工作中，我们提出了一个受约束的多目标优化框架（Opti-promp），以解决在紧凑型混乱中达到目标的问题，并利用了基于本地优化的策划者chomp的案例研究，该案例研究了簇中生长的软果。 Opti-promp特征与目标社区中静态，动态和可推动对象相关的成本，并且它依赖于概率基础来进行问题初始化。我们在模拟的多孔中分别在模拟的多孔中，基于文献和Opti-promp的Promp计划者，分别在低（3-DOFS）和高（7道）敏捷机器人机体上测试。结果表明，除了成功的静态障碍物避免和从可推动的质量中心的静电障碍物避免和系统的漂移外，还与7-DOFS机器人运动学的碰撞和推动成本最小化。
The majority of motion planning strategies developed over the literature for reaching an object in clutter are applied to two dimensional (2-d) space where the state space of the environment is constrained in one direction. Fewer works have been investigated to reach a target in 3-d cluttered space, and when so, they have limited performance when applied to complex cases. In this work, we propose a constrained multi-objective optimization framework (OptI-ProMP) to approach the problem of reaching a target in a compact clutter with a case study on soft fruits grown in clusters, leveraging the local optimisation-based planner CHOMP. OptI-ProMP features costs related to both static, dynamic and pushable objects in the target neighborhood, and it relies on probabilistic primitives for problem initialisation. We tested, in a simulated poly-tunnel, both ProMP-based planners from literature and the OptI-ProMP, on low (3-dofs) and high (7-dofs) dexterity robot body, respectively. Results show collision and pushing costs minimisation with 7-dofs robot kinematics, in addition to successful static obstacles avoidance and systematic drifting from the pushable objects center of mass.
