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.

提出了一种基于约束的多目标优化框架（OptI-ProMP）和基于局部优化的CHOMP规划器相结合的方法，用于解决在3D杂乱环境中通过多关节数学模型的机器人达到目标物体的问题，并与文献中的ProMP规划器进行比较，结果显示7个DoFs机器人运动学能最小化碰撞和推动成本，成功地完成了静态障碍物的避免和从可推动物体的质心系统漂移。

基于环境感知的互动运动原语在杂乱环境中用于物体抓取