Benchmarking motion planning algorithms for bin-picking applications

Purpose - For robot motion planning there exists a large number of different algorithms, each appropriate for a certain domain, and the right choice of planner depends on the specific use case. The purpose of this paper is to consider the application of bin picking and benchmark a set of motion planning algorithms to identify which are most suited in the given context. Design/methodology/approach - The paper presents a selection of motion planning algorithms and defines benchmarks based on three different bin-picking scenarios. The evaluation is done based on a fixed set of tasks, which are planned and executed on a real and a simulated robot. Findings - The benchmarking shows a clear difference between the planners and generally indicates that algorithms integrating optimization, despite longer planning time, perform better due to a faster execution. Originality/value - The originality of this work lies in the selected set of planners and the specific choice of application. Most new planners are only compared to existing methods for specific applications chosen to demonstrate the advantages. However, with the specifics of another application, such as bin picking, it is not obvious which planner to choose.