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.

翻译：基座放置优化（BPO）是移动操作领域的一项基础能力，已有数十年的研究历史。然而，该任务仍面临诸多挑战。首先，与人类相比，当前机器人缺乏灵活性，因此对基座放置（BP）精度要求更高。其次，基座放置与任务约束相互耦合：最优基座放置取决于任务约束，而基座放置又会影响任务约束。更棘手的是，部分任务约束具有灵活性和非确定性。第三，除完成任务外，还需考虑能耗最优、执行时间最小化等性能指标，这使基座放置优化问题更加复杂。本文采用基于类圆盘（SLD）的工作空间表示方法解耦任务约束与基座放置。为评估可达性并返回SLD上的最优工作姿态，离线构建了可达性地图（RM）。为同时优化覆盖范围、可操作性与时间成本目标，本文将基座放置优化建模为多目标优化问题（MOOP）。其中，时间最优目标被建模为旅行商问题（TSP），更符合实际场景。采用进化方法求解该多目标优化问题。此外，为保证解的有效性与最优性，对候选基座放置进行碰撞检测，并根据具体任务对基座放置优化解进行微调。最后，将所提方法应用于真实马桶覆盖清洁任务。实验表明，优化后的基座放置能显著提升任务覆盖范围与效率。