Evaluation of a Direct Optimization Method for Trajectory Planning of a 9-DOF Redundant Fruit-Picking Manipulator

Selective tasks such as harvesting or spraying of single crops are a promising research topic in agricultural automation. Inspired by industrial production, an obvious approach is to use robot manipulators in greenhouses and orchards. To exploit the potential of redundant manipulators in particular, advanced motion planning algorithms are needed. While harvesting, a new trajectory for every fruit has to be planned. Although the scenario is similar for every fruit, it is unique for each harvesting sequence. In this paper we present an efficient online planning approach which takes advantage of a simplified environment model. However, the generated trajectory is not optimal in general w.r.t. joint velocities or might even be unfeasible. Thus, we introduce an optional global offline optimization scheme which is able to find optimal trajectories in a few seconds and takes advantage of the heuristic planning as initial guess. We apply the proposed scheme to a 9-DOF agricultural manipulator for sweet-pepper harvesting and evaluate our method by extensive tests with fruit positions based on real measurements.