  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.
