  Base placement optimization (BPO) is a fundamental capability for mobile
manipulation and has been researched for decades. However, it is still very
challenging for some reasons. First, compared with humans, current robots are
extremely inflexible, and therefore have higher requirements on the accuracy of
base placements (BPs). Second, the BP and task constraints are coupled with
each other. The optimal BP depends on the task constraints, and in BP will
affect task constraints in turn. More tricky is that some task constraints are
flexible and non-deterministic. Third, except for fulfilling tasks, some other
performance metrics such as optimal energy consumption and minimal execution
time need to be considered, which makes the BPO problem even more complicated.
In this paper, a Scale-like disc (SLD) representation of the workspace is used
to decouple task constraints and BPs. To evaluate reachability and return
optimal working pose over SLDs, a reachability map (RM) is constructed offline.
In order to optimize the objectives of coverage, manipulability, and time cost
simultaneously, this paper formulates the BPO as a multi-objective optimization
problem (MOOP). Among them, the time optimal objective is modeled as a
traveling salesman problem (TSP), which is more in line with the actual
situation. The evolutionary method is used to solve the MOOP. Besides, to
ensure the validity and optimality of the solution, collision detection is
performed on the candidate BPs, and solutions from BPO are further fine-tuned
according to the specific given task. Finally, the proposed method is used to
solve a real-world toilet coverage cleaning task. Experiments show that the
optimized BPs can significantly improve the coverage and efficiency of the
task.
