Motion Planning : Wild Frontiers
Motion Planning : Wild Frontiers
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运动规划：狂野前沿
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发表时间：
2011
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通讯作者：
S. LaValle
中科院分区：
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作者：
S. LaValle
计算机器人在已知障碍物中无碰撞路径的基本问题已经被很好地理解和合理地解决了；然而，问题表述本身的不足和自主系统设计中工程挑战的需求为未来的研究提出了重要的问题和课题。当考虑如何在机器人系统中典型地使用计算路径时，基本路径规划的缺点变得清晰可见。几十年来，人们已经知道，有效的自治系统必须迭代地感知新数据并采取相应的行动；回想一下几十年前的《感官计划法案》（SPA）范式。图1显示了计算的无碰撞路径τ:[0,1]→Cfree通常是如何通过产生反馈控制律来与该视图对齐的。步骤1使用路径规划算法生成τ。step2然后smoothstτ得到σ:[0,1]→Cfree，这是机器人实际可以遵循的路径。例如，如果路径是分段线性的，那么像汽车一样的移动机器人将无法转弯。步骤3重新参数化σ，使轨迹q: [0， tf]→Cfree，名义上满足机器人动力学（例如，加速度边界）。在步骤4中，设计一个状态反馈控制律，在执行过程中尽可能密切地跟踪q / a。这就产生了一个策略或计划，π: X→U。域x是一个状态空间（或阶段空间），U是一个动作空间（或输入空间）。这些集合出现在机器人建模的控制系统的定义中：x = f(x, u)，其中x∈x， u∈u。这个通用框架中一个明显的问题是，由于前一个步骤中不幸的固定选择，后一个步骤可能无法成功。即使它真的成功了，产生的解决方案也可能是非常低效的。这激发了不同约束下的计划，即执行步骤1和步骤2，或者一次性执行步骤1、2和3；见第二节。第4步最终需要反馈，这促使我们直接计算反馈计划，详见第三节。图1中框架的另一个问题（可能更微妙）是，这种对机器人导航的整体问题的固定分解人为地夸大了信息需求。该框架网络要求传感器功能强大，结合强度强
The basic problem of computing a collision-free path for a robot among known obstacles is well-understood and reasonably well-solved; however, deficiencies in the probl em formulation itself and the demand of engineering challenge s in the design of autonomous systems raise important questions and topics for future research. The shortcomings of basic path planning become clearly visible when considering how the computed path is typically used in a robotic system. It has been known for decades that effective autonomous systems must iteratively sensenew data and act accordingly; recall the decades-old Sense Plan Act (SPA) paradigm. Figure 1 shows how a computed collisionfree pathτ : [0, 1]→ Cfree is usually brought into alignment with this view by producing a feedback control law. Step 1 producesτ using a path planning algorithm. Step 2 then smoothsτ to produceσ : [0, 1] → Cfree, a path that the robot can actually follow. For example, if the path is piecewise linear, then a car-like mobile robot would not be able to turn sharp corners. Step 3 reparameterizes σ to make a trajectory q̃ : [0, tf ] → Cfree that nominally satisfies the robot dynamics (for example, acceleration bounds). In Step 4, a state-feedback control law is designed that tracks q̃ a closely as possible during execution. This results in a policy or plan, π : X → U . The domainX is a state space(or phase space ) and U is an action space(or input space). These sets appear in the definition of the control system that models the robot:̇ x = f(x, u) in which x ∈ X andu ∈ U . One clear problem in this general framework is that a later step might not succeed due to an unfortunate, fixed choice in an earlier step. Even if it does succeed, the produced soluti on may be horribly inefficient. This motivates planning under differential constraints , which essentially performs Steps 1 and 2, or Steps 1, 2, and 3 in one shot; see Section II. The eventual need for feedback in Step 4 motivates the direct computation of afeedback plan , covered in Section III. Another issue with the framework in Figure 1, which is perhaps more subtle, is that this fixed decomposition of the overall problem of getting a robot to navigate has artificially inflated the information requirements. The fra mework requires that powerful sensors, combined with strong