We present a modular Bayesian optimization framework that efficiently generates time-optimal trajectories for a cooperative multi-agent system, such as a team of UAVs. Existing methods for multi-agent trajectory generation often rely on overly conservative constraints to reduce the complexity of this high-dimensional planning problem, leading to suboptimal solutions. We propose a novel modular structure for the Bayesian optimization model that consists of multiple Gaussian process surrogate models that represent the dynamic feasibility and collision avoidance constraints. This modular structure alleviates the stark increase in computational cost with problem dimensionality and enables the use of minimal constraints in the joint optimization of the multi-agent trajectories. The efficiency of the algorithm is further improved by introducing a scheme for simultaneous evaluation of the Bayesian optimization acquisition function and random sampling. The modular BayesOpt algorithm was applied to optimize multi-agent trajectories through six unique environments using multi-fidelity evaluations from various data sources. It was found that the resulting trajectories are faster than those obtained from two baseline methods. The optimized trajectories were validated in real-world experiments using four quadcopters that fly within centimeters of each other at speeds up to 7.4 m/s.
翻译:我们提出了一个模块化贝叶斯优化框架,为合作性多试剂系统(如无人驾驶飞行器小组)有效创造最理想的时间轨迹。多试剂轨迹生成的现有方法往往依靠过于保守的限制来降低这一高维规划问题的复杂性,从而导致次优化解决方案。我们为贝叶斯优化模型提出了一个新型模块结构,由代表动态可行性和避免碰撞制约的多个高斯进程替代模型组成。这一模块结构缓解了计算成本的急剧增加,使多试剂轨迹的联合优化能够使用最低限度的限制。通过引入一个同时评价贝叶斯优化获取功能和随机抽样的方案,进一步提高了算法的效率。模块化贝叶斯奥普特算法应用了六种独特的环境来优化多试管轨迹,这六种环境代表了动态可行性和避免碰撞的制约。发现由此产生的轨迹比从两种基线方法中获得的轨迹更快。在现实世界中,每个优化的轨迹轨迹都用四度模型验证了每个飞行速度/速度的图像。