Momentum Based Motion Planning for Manipulators with Heavy Loads

本研究的重点是发展计算效率高的运动规划算法，使机械手机器人，以显着增加其起重能力比目前的大多数算法，这只能使机器人升降机负载，可以支持静态在每个点沿着的机械手path. In更一般的意义上，这项研究是关注基于动量的运动规划使用动态模型。该规划将使用最近开发的运动规划算法，基于采样的模型预测优化（SBMPO），这可以被看作是一个基于采样的A* 算法的泛化，使用动态模型的最佳运动规划来完成。 该研究将为与使用SBMPO直接开发具有适当动量特性的轨迹相关的基本问题提供答案。一个是什么模型-例如开环动态模型，闭环动态模型，甚至是一个？扩展运动学模型(i.e.,前面有一个或多个积分器的运动学模型）-在结合关于操纵器动力学和扭矩极限的信息方面是最有效的吗？一个相关的关键问题是发展？乐观主义者？(i.e所选成本的严格下限），其不是过度保守的，因此可以允许有效的轨迹生成。 .一个主要的贡献也将是纳入学习，以便当一个解除或投掷问题是遇到类似于以前解决的问题，规划算法根据先前的经验执行得更快。由于当前的规划算法是根据运动学模型生成路径的，而这些运动学模型不能模拟电动机的扭矩限制，因此机械手的提升和投掷能力没有得到充分利用或负载机械手的惯性特性，因此不能规划具有提升或投掷重负载所需动量的轨迹。这项研究提供了运动规划算法，增加任何机械手，有一个控制系统，旨在遵循轨迹的加速度，速度和位置方面的提升和投掷能力。其主要应用是服务机器人（例如，类人机器人），其与其工业对应物相比重量轻且相对较弱。这些机器人将能够执行重要的起重任务，例如在家庭环境中提起行李，杂货，一加仑牛奶罐和书包。这项研究还将提高机器人清理室外重物的能力，如原木、砖块和大块岩石。预计如果机器人被赋予这种增加的能力，其他应用将随之而来。这项研究将适用于移动的机器人，以及由于陡峭的山丘，粘性泥或深沙补丁，高刚性植被的环境需要基于动量的规划。 在一般情况下，这项研究将有助于连接控制和规划领域的新方式，与主要动机是可靠的和计算效率的轨迹生成算法的发展势头为基础的规划。

This research focuses on the development of computationally efficient motion planning algorithms that enable a manipulator robot to dramatically increase its lifting capacity over most current algorithms, which only enable a robot to lift loads that can be supported statically at each point along the manipulator path. In a more general sense, this research is concerned with momentum based motion planning using dynamic models. The planning will be accomplished using a recently developed motion planning algorithm, Sampling Based Model Predictive Optimization (SBMPO), which may be viewed as a sampling-based generalization of the A* algorithm to optimal motion planning using a dynamic model. The research will develop answers to fundamental questions related to the use of SBMPO for the direct development of trajectories that have appropriate momentum characteristics. One is what model - for example the open loop dynamic model, the closed loop dynamic model, or even an ?extended kinematic model? (i.e., a kinematic model preceded by one or more integrators) - is the most effective at incorporating information about the manipulator dynamics and torque limits? A related critical issue is the development of ?optimistic A* heuristics? (i.e, rigorous lower bounds on the chosen cost) that are not overly conservative and hence can allow for efficient trajectory generation. . A major contribution will also be the incorporation of learning, so that when a lifting or throwing problems is encountered that is similar to a previously solved problem, the planning algorithm executes more quickly based on the prior experienceManipulator lifting and throwing capabilities are considerably underutilized because current planning algorithms generate paths based on kinematic models that are not capable of modeling the torque limitations of the motors or the inertial characteristics of the loaded manipulator and hence cannot plan trajectories with the required momentum to lift or throw heavy loads. This research provides motion planning algorithms that increase the lifting and throwing capacity of any manipulator that has a control system designed to follow trajectories characterized in terms of acceleration, velocity, and position. A primary application of this is service robots (e.g., humanoid robots), which are light weight and relatively weak compared to their industrial counterparts. These robots will be able to perform important lifting tasks such as the lifting of luggage, groceries, one gallon milk jugs, and book bags in home environments. The research will also increase the capacity of robots used to clear heavy outdoor debris such as logs, bricks, and large rocks. It is expected that if a robot is given this increased capacity, other applications will follow. This research will be applicable to mobile robotics as well since environments with steep hills, viscous mud or deep sand patches, and high stiff vegetation require momentum based planning. In general, this research will help to connect the fields of control and planning in new ways, with the primary motivation being the development of reliable and computationally efficient trajectory generation algorithms for momentum based planning.