ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (2): 26495-026495.doi: 10.7527/S1000-6893.2021.26495
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Yongxing TANG1,2, Zhanxia ZHU1,2(), Hongwen ZHANG3, Jianjun LUO1,2, Jianping YUAN1,2
Received:
2021-10-09
Revised:
2021-10-27
Accepted:
2021-11-18
Online:
2023-01-25
Published:
2021-12-09
Contact:
Zhanxia ZHU
E-mail:zhuzhanxia@nwpu.edu.cn
Supported by:
CLC Number:
Yongxing TANG, Zhanxia ZHU, Hongwen ZHANG, Jianjun LUO, Jianping YUAN. A tutorial and review on robot motion planning[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(2): 26495-026495.
Table 2
Summary of acceleration strategies or solution quality improvement strategies for SBMP
问题类型 | 可用信息 | 技术描述 | 相关文献 |
---|---|---|---|
传统运动规划 | 确定性采样点集或序列 | 用确定性采样过程代替随机采样,保障算法的确定性渐进最优性或分辨率完备性,改善收敛率、计算复杂度和时间复杂度 | [ |
Cfree形状信息 | 根据推测得到的Cfree形状,增加与可行路径解相关的困难区域的采样点数量 | [ | |
解路径代价或尚需代价的估值 | 用已有的解路径代价限制SBMP的搜索区域,达到加速算法的目的 | [ | |
用尚需代价的估值安排搜索次序,克服早期SBMP算法盲目搜索的缺点 | [ | ||
之前有效的搜索信息或由传感器获得的动态障碍物运动信息 | 从目标构型反向搜索;遭遇障碍物后,保留之前的部分有效信息,并在此基础上增强采样树(路图),以减少重规划的时间 | [ | |
预测动态障碍物的行为或轨迹,以提高解品质,降低重规划频次 | [ | ||
编码C-space信息、最优路径信息的训练数据集 | 用数据集训练CAE,CVAE,CNN,GNN等,以为SBMP提供采样点或直接生成可行(近优)路径 | [ | |
在与环境交互中学习到的信息 | 将强化学习与SBMP结合 | [ | |
考虑微分约束的开环运动规划 | 状态的可达性信息、状态扩展后的碰撞概率、离散动作集中已使用过的动作信息、采样树对搜索空间的覆盖信息等 | 在使用较差的度量函数的情况下,利用这些信息引导采样树生长,使其能较快地对初始状态的无碰可达集实现稠密覆盖 | [ |
解轨迹的代价信息、近似可达集信息、启发式信息 | 类似于在传统运动规划算法中的做法,拒绝潜在解轨迹代价大于已有解轨迹代价的采样点;或直接在近似可达集中采样;安排算法的搜索次序 | [ | |
包含大量运动规划问题解信息的训练集 | 1) 端到端地生成轨迹;2) 学习在无碰可达集内生成稠密(最优)的采样点分布;3) 在不考虑障碍物的情况下,学习针对复杂系统的LPM;4) 学习有关NSM的度量函数 | [ |
Table 3
Summary of optimal algorithms considering differential constraints[78]
收敛性质 | 结构 | 算法 | 条件 | |
---|---|---|---|---|
传统运动规划 | 几乎一定 | 路图 | PRM* | |
概率性 | 路图上的搜索树 | FMT* | ||
概率性 | 带有Rewiring的树 | RRT* | ||
确定性,基于离散度 | 路图 | PRM* FMT* | 如果采样离散度为 | |
含微分约束的运动规划 | 概率性 | 前向搜索树 | SST* | 随机选择;蒙特卡洛传播:随机控制和随机积分时间 |
概率性 | 前向搜索树 | AO-RRT | 在增强的State-Cost空间中用RRT选择;蒙特卡洛传播 | |
概率性 | Meta算法 | AO-X | 重复调用有一个有递减代价界的概率完备算法X | |
CHOMP,STOMP | 用确定性协变方法或概率梯度下降方法求解无约束优化问题 | |||
部分算法提供了收敛性保证 | TrajOpt,SCvx,GuSTO | 建立恰当的障碍物约束;非凸问题的凸化 | ||
凸分解方法 | SFC的快速生成;时间分配;区间分配 |
Table 4
Summary of feedback motion planning algorithms
不确定性建模方式 | 算法 | 技术描述 | 相关文献 |
---|---|---|---|
显式方法 | 基于点的方法 | 离散模型;用信念空间中的采样点集近似表示信念空间,用离线值迭代方式求得在线执行所依赖的动作策略 | [ |
POMCP,DESPOT | 离散模型;将规划和执行规划结果在线交替进行;蒙特卡洛采样技术 | [ | |
POMCPOW,DESPOT-α | 采用一种受粒子滤波启发的加权方案,可较好应对具有连续观测空间的真实问题 | [ | |
BRM,RRBT,LQG-MP,FIRM | 结合了传统运动规划和随机最优控制;基于高斯噪声假设,存在一定的局限性 | [ | |
隐式方法 | FaSTrack | 利用H可达性分析离线计算并保存最大跟踪误差,并将使用简化模型进行实时规划的路径或轨迹规划器融入其中 | [ |
Funnel libraries | 利用SOS离线计算Funnels,然后通过在线序列组合达到避障的目的 | [ | |
基于CBF的算法 | 将软约束处理为CLF,将硬约束处理为CBF,并在二次规划的框架下进行求解 | [ | |
基于收缩理论的算法 | 利用SOS离线计算最优CCMs,从而得到尺寸固定、界面最小的不变Tube及与之相关的反馈跟踪控制器 | [ |
Table 5
Summary of advantages and disadvantages of various motion planning algorithms
运动规划问题的类型 | 算法类别 | 优势 | 劣势 |
---|---|---|---|
传统运动规划 | CMP | 算法具有完备性 | 不适用于高维运动规划问题和障碍物数量巨大的问题 |
SBMP | 1) 现实中大多数C-space都具有良好的可视特性是SBMP在较高维空间取得成功的基础,其主要优势在于用可数点集或序列及它们间的连接关系近似C-space的连通特性;2)对于基于采样的路图算法,利用启发式的A*及其变体可以减少计算时间 | 1)原始SBMP算法的搜索过程是无序的,这在某种程度上将问题的成功求解寄托于概率;2)随机性无法克服维数诅咒;3)原始算法的搜索范围被限定为整个C-space,浪费了大量计算时间;4)算法只能保证概率完备性和分辨率完备性;5)规划结果需要后处理 | |
考虑微分约束的开环运动规划 | 递增搜索与采样算法 | 1) 得益于自身的碰撞检测模块,避免了显式处理相空间中的障碍物约束;2) 可直接得到服从系统运动特性的轨迹;3)具有全局搜索特性 | 1)度量函数敏感性、随机搜索和搜索范围固定的特点使此类算法的实时性受到严重限制;2)求解复杂系统TPBVP问题的困难性导致此类算法的最优性条件无法满足 |
基于优化的运动规划算法 | 1) 可直接得到服从系统运动特性的轨迹;2)可借助优化领域大量的成熟工具进行求解;3)部分算法可以用于实时任务 | 1)对初始猜想敏感;2)算法的收敛特性很难得到保障;3)一般只能得到局部最优解;4)需显式处理障碍物约束;5)非凸问题的凸化较为困难 | |
反馈运动规划 | 不确定性的显式建模方法 | 相比隐式建模方法,规划结果的保守性较低 | 对建模的可靠性要求很强,而显式建立这样的模型往往非常复杂并具有挑战性,故很容易产生建模误差 |
不确定性的隐式建模方法 | 可在所考虑的不确定性范围内保证机器人运动的绝对安全 | 1)相比显式建模方法,规划结果有较强的保守性;2)辅助控制器的计算过程较为耗时,需离线进行 |
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