Certain species of ants can carry out tasks in dense work spaces while maintaining their ability to accurately manipulate heavy loads, and these advantages are of interest to the robotics community. We consider a robotic swarm of $N\ge 6$ agents that assumes the task of moving a load through a cluttered space. This forces the swarm to carefully manipulate the orientation of the load, while transporting it to its destination point. We model this scenario as a 6-PPSS (Prismatic-Prismatic-Spherical-Spherical) redundant mobile platform, having six degrees of freedom. As with insects, the multitude of agents enables sharing the burden of the load in the case that one or more agents are blocked by an obstacle. We model this by a semi-algebraic set of constraints on the distances between the agents and the load. We apply an Extended Kalman Filter routine, in order to estimate their relative locations. We show how the estimation-error is reduced when position-information is shared among the agents. These estimations are then used to calculate the full configuration and investigate the effect of position estimation error on the platform heading error. We show how motion planning can then be calculated in the model’s full configuration space and demonstrate this with a distributed control scheme. To reduce the search time, we introduce a variant of the crawling probabilistic road map motion planning algorithm under a set of kinematic constraints and work-space obstacles. Finally, we exemplify our algorithms on several simulated scenarios.

中文翻译：

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用于承载和操纵的机器人群体运动规划


某些种类的蚂蚁可以在密集的工作空间中执行任务，同时保持准确操纵重物的能力，这些优点引起了机器人界的兴趣。我们考虑由 $N\ge 6$ 代理组成的机器人群，承担在杂乱空间中移动负载的任务。这迫使蜂群在将负载运输到目的地的同时小心地操纵负载的方向。我们将此场景建模为 6-PPSS（棱柱-棱柱-球面-球面）冗余移动平台，具有六个自由度。与昆虫一样，在一个或多个代理被障碍物阻挡的情况下，多个代理可以分担负载负担。我们通过代理和负载之间距离的半代数约束集对此进行建模。我们应用扩展卡尔曼滤波器例程，以估计它们的相对位置。我们展示了当代理之间共享位置信息时如何减少估计误差。然后使用这些估计来计算完整配置并研究位置估计误差对平台航向误差的影响。我们展示了如何在模型的完整配置空间中计算运动规划，并通过分布式控制方案进行演示。为了减少搜索时间，我们在一组运动学约束和工作空间障碍物下引入了爬行概率路线图运动规划算法的变体。最后，我们在几个模拟场景中举例说明了我们的算法。