Robot Return to Origin

There is a robot starting at position (0, 0), the origin, on a 2D plane. Given a sequence of its moves, judge if this robot ends up at (0, 0)after it completes its moves.

The move sequence is represented by a string, and the character moves[i] represents its ith move. Valid moves are R (right), L (left), U (up), and D (down). If the robot returns to the origin after it finishes all of its moves, return true. Otherwise, return false.

Note: The way that the robot is "facing" is irrelevant. "R" will always make the robot move to the right once, "L" will always make it move left, etc. Also, assume that the magnitude of the robot's movement is the same for each move.

Example 1:

Input: "UD"
Output: true 
Explanation: The robot moves up once, and then down once. All moves have the same magnitude, so it ended up at the origin
where it started. Therefore, we return true.

 

Example 2:

Input: "LL"
Output: false
Explanation: The robot moves left twice. It ends up two "moves" to the left of the origin. We return false because i

Python3

 1  1 class Solution:
 2  2     def judgeCircle(self, moves):
 3  3         """
 4  4         :type moves: str
 5  5         :rtype: bool
 6  6         """
 7  7         x = 0
 8  8         y = 0
 9  9         for s in moves:
10 10             if s == 'U':
11 11                 y += 1
12 12             elif s == 'D':
13 13                 y -= 1
14 14             elif s == 'R':
15 15                 x += 1
16 16             elif s == 'L':
17 17                 x -= 1
18 18         if (x, y) == (0, 0):
19 19             return True
20 20         else:
21 21             return False

 

转载于:https://www.cnblogs.com/locke-hu/p/9628107.html

function [robot,T_Ln_EE] = DH_Serial2robot(DH_data) % Transforms a description of a multibody system, provided in Denavit-Hartenberg parameters, into the SPART robot model. % % [robot,T_Ln_EE] = DH_Serial2robot(DH_data) % % :parameters: % * DH_data -- Structure containing the DH parameters. (see :doc:`/DH`). % % :return: % * robot -- Robot model (see :doc:`/Tutorial_Robot`). % * T_Ln_EE -- Homogeneous transformation matrix from last link to end-effector --[4x4]. % % **DH descriptions are only supported for serial configurations**. %{ LICENSE This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. %} %=== CODE ===% %Create Robot structure. robot=struct(); %Get name if isfield(DH_data,'name') robot.name=DH.data.name; else robot.name='DH robot'; end %Number of links and joints robot.n_links_joints=DH_data.n; robot.n_q=DH_data.n; %Create links and joints structures robot.links=struct(); robot.joints=struct(); robot.base_link=struct(); %Assign origin robot.origin='DH'; %--- Base link ---% %Get name robot.base_link.name='Base'; %Homogeneous transformation robot.base_link.T=[eye(3),zeros(3,1);zeros(1,3),1]; %Mass and inertia robot.base_link.mass=DH_data.base.mass; robot.base_link.inertia=DH_data.base.I; %--- Iterate through joints and links ---% for i=1:DH_data.n %--- Links ---% %ID robot.links(i).id=i; %Get name
03-19
### Origin Car 的避障功能及其实现方法 Origin Car 是一种常见的移动机器人平台,通常具备基本的导航和避障能力。以下是关于 Origin Car 避障算法的具体实现方案: #### 1. **传感器配置** 为了实现有效的避障功能,Origin Car 通常配备有多种类型的传感器,例如超声波传感器、红外传感器或激光雷达等。这些传感器可以实时感知周围环境的距离信息[^3]。 - 超声波传感器适合短距离测距,具有成本低的优点。 - 激光雷达提供更高精度的距离测量数据,尤其适用于复杂的动态环境中。 #### 2. **坐标系定义与姿态估计** 当 Origin Car 启动时,会初始化一个固定的世界坐标系(frame),该坐标系原点位于机器人的中心位置,正方向沿着车体前部指向前方。机器人通过轮子转动来更新自身的位姿(pose)。位姿由 \(x\) 和 \(y\) 表示的位置以及 \(\theta\) 表示的方向角组成[^3]。 需要注意的是,由于轮子打滑等原因,里程计误差可能会随着时间累积而增大,因此需要定期校准或者引入其他定位手段如 SLAM 技术来进行补偿。 #### 3. **路径规划与 RRT 算法的应用** 对于复杂障碍物分布的情况,可以采用快速扩展随机树 (RRT) 方法进行全局路径规划。RRT 算法通过构建一颗逐步生长的随机树结构,在高维度空间内寻找从起点至终点的有效通路[^1]。此过程涉及以下几个方面: - **节点采样**: 在自由空间中随机选取样本点作为候选扩展节点; - **最近邻查找**: 寻找当前已建立树中最靠近新选定点的一个已有结点; - **连接操作**: 尝试将两者相连形成新的分支;如果遇到阻碍则放弃此次尝试继续迭代直到满足终止条件为止。 #### 4. **碰撞检测机制** 在执行具体动作之前还需要完成必要的碰撞预测工作以确保安全性。这可以通过几何模型分析物体间可能发生的接触情况并及时调整计划好的轨迹路线来达成目的[^2]。简单的矩形包围盒测试或是更精确但计算开销较大的多项式曲面拟合都可以用来判断是否存在潜在冲突风险。 ```python def collision_detection(robot_pose, obstacle_list): for obs in obstacle_list: distance = calculate_distance(robot_pose, obs.position) if distance < safety_threshold: return True # Collision detected return False # No collision ``` 以上代码片段展示了一个基础版本的碰撞探测函数逻辑框架。 --- ###
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