An insect-inspired approach to robotic grasping

为了真正有用，机器人需要与世界上的物体互动。目前，机器人无法高效、可靠地抓取各种物体，严重限制了机器人的应用范围。农业、采矿和环境清理只是三个例子，与工厂不同，要处理的物品可能有各种各样的形状和外观，需要在杂乱中识别，需要在运输时牢牢抓住，同时避免损坏。尽管在三维传感和重建、机械臂复杂性和最近大规模机器学习的使用方面有所改进，但对机器人来说，在杂乱中安全地抓取未知物体仍然是一个未解决的问题。这个项目提出了一种新的方法，灵感来自于蚂蚁在执行类似的收集和操纵各种食物的任务时所表现出的高能力。蚂蚁有相对简单的，类似机器人的“抓手”（它们的嘴部，称为“下颌骨”），有限的感知（主要是触觉，使用它们的触角）和微小的大脑。然而，它们能够拾取和携带各种各样的食物，从种子到其他昆虫猎物，这些食物在形状、大小、硬度和机动性上都有很大差异。他们可以在多个项目之间快速选择，并找到一个有效的位置进行抓握，必要时重新调整。将这种能力的一部分复制到机器人身上将是一项重大进步。因此，抓取是应用生物机器人方法的理想目标，我的团队之前已经成功地使用了生物机器人方法来理解和模仿机器人上的昆虫导航行为。蚂蚁是如何拾取物体的？该项目的第一部分将是建立所需的方法来详细观察和分析蚂蚁与物体相互作用的行为。与此同时，我们将开始建立模拟和真实的机器人系统，使我们能够模仿蚂蚁的动作，因为它的身体，头部和嘴的位置进行抓握；采用全向机器人底座，配有手臂和夹持器。我们还将研究和模仿蚂蚁在抓住物体之前确定物体位置、形状和大小的感觉系统。当蚂蚁拿起一个物体时，它的大脑会发生什么？第二部分将探讨昆虫大脑需要计算哪些算法才能做出高效的抓取决策。抓取是机器人智能中包含许多关键问题的一项任务。它涉及物理、感知和控制系统的紧密耦合。它涉及控制决策的层次结构（是否抓取，如何定位身体和执行器，精确接触，处理不确定性，检测故障）。它需要将感官信息融合并转化为动作状态空间，涉及预测、规划和适应。我们的目标是了解昆虫如何解决这些问题，从而为机器人技术提供高效的解决方案。机器人的表现能和蚂蚁一样好吗？最后一部分将在现实世界的任务中测试我们开发的系统。第一个任务将是执行对象清理任务，这也将允许将开发的系统与现有研究进行基准测试。第二项任务将基于环境清理中的紧迫问题：从海岸线岩石和砾石中检测和清除小塑料物品。这一新的研究领域有望从将生物学理解转化为技术进步中获得重大回报，因为它解决了一个重要的未解决的挑战，蚂蚁是一个理想的动物模型。

To be really useful, robots need to interact with objects in the world. The current inability of robots to grasp diverse objects with efficiency and reliability severely limits their range of application. Agriculture, mining and environmental clean-up arejust three examples where - unlike a factory - the items to be handled could have a huge variety of shapes and appearances, need to be identified amongst clutter, and need to be grasped firmly for transport while avoiding damage. Secure grasp of unknown objects amongst clutter remains an unsolved problem for robotics, despite improvements in 3Dsensing and reconstruction, in manipulator sophistication and the recent use of large-scale machine learning.This project proposes a new approach inspired by the high competence exhibited by ants when performing the closely equivalent task of collecting and manipulating diverse food items. Ants have relatvely simple, robot-like 'grippers' (their mouth-parts, called 'mandibles'), limited sensing (mostly tactile, using their antennae) and tiny brains. Yet they are able to pick up and carry a wide diversity of food items, from seeds to other insect prey, which can vary enormously in shape, size, rigidity and manouverability. They can quickly choose between multiple items and find an effective position to make their grasp, readjusting if necessary. Replicating even part of this competence on robots would be a significant advance. Grasping thus makes an ideal target for applying biorobotic methods that my group has previously used with substantial success to understand and mimic insect navigation behaviours on robots.How does an ant pick up an object? The first part of this project will be to set up the methods required to observe and analyse in detail the behaviour of ants interacting with objects. At the same time we will start to build both simulated and real robot systems that allow us to imitate the actions of an ant as it positions its body, head and mouth to make a grasp; using an omnidirectional robot base with an arm and gripper. We will also examine and imitate the sensory systems usedby the ant to determine the position, shape and size of the object before making a grasp.What happens in the ant's brain when it picks up an object? The second part will explore what algorithms insect brains need to compute to be able to make efficient and effective grasping decisions. Grasping is a task that contains in miniature many key issues in robot intelligence. It involves tight coupling of physical, perceptual and control systems. It involves a hierarchy of control decisions (whether to grasp, how to position the body and actuators, precise contact, dealing with uncertainty, detecting failure). It requires fusion of sensory information and transformation into the action state space, and involves prediction, planning and adaptation. We aim tounderstand how insects solve these problems as a route to efficient and effective solutions for robotics.Can a robot perform as well as an ant? The final part will test the systems we have developed in real world tasks. The first task will be to perform an object clearing task, which will also allow benchmarking of the developed system against existing research. The second task will be based ona pressing problem in environmental clean-up: detection and removal of small plastic items from amongst shoreline rocksand gravel. This novel area of research promises significant pay-off from translating biological understanding into technical advance because it addresses an important unsolved challenge for which the ant is an ideal animal model.