动态窗口方法 (Dynamic Window Approach)
在空间下，确定
- 静态窗口（最大/最小速度&角速度）
- 动态窗口（由当前速度+最大加速度&角加速度所能达到的速度）
- 无碰撞范围
在此范围内确定最优方案，评价指标包括
- 当前朝向与目标方向的偏差
- 与目标点的距离
- 速度（在终点刹车范围外越快越好）
缺点
- 目标函数局部最优
- 假设障碍物静止
改进一：运动障碍物 Velocity Obstacles
改进二：协作避障Reciprocal Velocity Obstacles
- 坐标系为两个机器人之间的相对速度和角速度
- 如当前速度和角速度在障碍区域内，作当前点到障碍区域切线的垂线，两个机器人沿着垂线方向各走至少一半。
图搜索
Open Set: Unexpanded Nodes
Closed: 已访问的，不再更新
广度优先
- Complete (will find the solution if it exists)
- First solution found is the optimal path
深度优先
- Lower memory footprint than BFS with high-branching
- DFS not complete for infinite trees
Dijkstra’s Algorithm
- Open queue is ordered according to currently known best cost to arrive
A* Heuristic Search
The cost function is a sum of two functions:
- Past path-cost function, which is a known cost from the starting node to the current node
- Future path-cost function, which is a “heuristic estimate” of the distance from the current node to the goal
SAMPLING-BASED MOTION PLANNING
- Sampling algorithm
- distance function
- collision detection approach
Probabilistic Road Maps
- Step 1: Build a roadmap by connecting nearby (sampled, free-space) configurations using simple planners to construct a graph of valid path segments
- Step 2: Query: Search the graph using a graph search technique (A*)
- 适用于multiple-queries
Rapidly Exploring Dense Trees
- 适用于single-query
- 节点之间具有方向性
- 易于与机器人的运动限制相结合