In recent years, nonlinear model predictive control (NMPC) has been extensively used for solving automotive motion control and planning tasks. In order to formulate the NMPC problem, different coordinate systems can be used with different advantages. We propose and compare formulations for the NMPC related optimization problem, involving a Cartesian and a Frenet coordinate frame (CCF/ FCF) in a single nonlinear program (NLP). We specify costs and collision avoidance constraints in the more advantageous coordinate frame, derive appropriate formulations and compare different obstacle constraints. With this approach, we exploit the simpler formulation of opponent vehicle constraints in the CCF, as well as road aligned costs and constraints related to the FCF. Comparisons to other approaches in a simulation framework highlight the advantages of the proposed approaches.
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We present an approach for safe trajectory planning, where a strategic task related to autonomous racing is learned sample-efficient within a simulation environment. A high-level policy, represented as a neural network, outputs a reward specification that is used within the cost function of a parametric nonlinear model predictive controller (NMPC). By including constraints and vehicle kinematics in the NLP, we are able to guarantee safe and feasible trajectories related to the used model. Compared to classical reinforcement learning (RL), our approach restricts the exploration to safe trajectories, starts with a good prior performance and yields full trajectories that can be passed to a tracking lowest-level controller. We do not address the lowest-level controller in this work and assume perfect tracking of feasible trajectories. We show the superior performance of our algorithm on simulated racing tasks that include high-level decision making. The vehicle learns to efficiently overtake slower vehicles and to avoid getting overtaken by blocking faster vehicles.
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In order for automated mobile vehicles to navigate in the real world with minimal collision risks, it is necessary for their planning algorithms to consider uncertainties from measurements and environmental disturbances. In this paper, we consider analytical solutions for a conservative approximation of the mutual probability of collision between two robotic vehicles in the presence of such uncertainties. Therein, we present two methods, which we call unitary scaling and principal axes rotation, for decoupling the bivariate integral required for efficient approximation of the probability of collision between two vehicles including orientation effects. We compare the conservatism of these methods analytically and numerically. By closing a control loop through a model predictive guidance scheme, we observe through Monte-Carlo simulations that directly implementing collision avoidance constraints from the conservative approximations remains infeasible for real-time planning. We then propose and implement a convexification approach based on the tightened collision constraints that significantly improves the computational efficiency and robustness of the predictive guidance scheme.
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作为自动驾驶系统的核心部分,运动计划已受到学术界和行业的广泛关注。但是,由于非体力学动力学,尤其是在存在非结构化的环境和动态障碍的情况下,没有能够有效的轨迹计划解决方案能够为空间周期关节优化。为了弥合差距,我们提出了一种多功能和实时轨迹优化方法,该方法可以在任意约束下使用完整的车辆模型生成高质量的可行轨迹。通过利用类似汽车的机器人的差异平坦性能,我们使用平坦的输出来分析所有可行性约束,以简化轨迹计划问题。此外,通过全尺寸多边形实现避免障碍物,以产生较少的保守轨迹,并具有安全保证,尤其是在紧密约束的空间中。我们通过最先进的方法介绍了全面的基准测试,这证明了所提出的方法在效率和轨迹质量方面的重要性。现实世界实验验证了我们算法的实用性。我们将发布我们的代码作为开源软件包,目的是参考研究社区。
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在自主驾驶的背景下,已知迭代线性二次调节器(ILQR)是在运动计划问题中处理非线性车辆模型的有效方法。特别是,受约束的ILQR算法在不同类型的一般限制下实现运动计划任务方面表现出了值得注意的计算效率结果。但是,受约束的ILQR方法需要在使用对数屏障函数时在第一次迭代时作为先决条件进行可行的轨迹。同样,该方法为纳入快速,高效和有效的优化方法开辟了可能性,以进一步加快优化过程,从而可以成功地满足实时实施的要求。在本文中,定义明确的运动计划问题是在非线性车辆动力学和各种约束下提出的,并利用了乘数的交替方向方法来确定利用ILQR的最佳控制动作。该方法能够在第一次迭代时规避轨迹的可行性要求。然后研究了自动驾驶汽车运动计划的说明性示例。拟议的开发实现了高度计算效率的值得注意的成就。与基于对数屏障函数的约束ILQR算法进行比较,我们提出的方法在三种驾驶场景中,平均计算时间降低了31.93%,38.52%和44.57%;与优化求解器IPOPT相比,我们提出的方法将平均计算时间降低了46.02%,53.26%和88.43%。结果,可以通过我们提出的框架实现实时计算和实施,因此它为公路驾驶任务提供了额外的安全性。
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延迟在迅速变化的环境中运行的自主系统的危害安全性,例如在自动驾驶和高速赛车方面的交通参与者的非确定性。不幸的是,在传统的控制器设计或在物理世界中部署之前,通常不考虑延迟。在本文中,从非线性优化到运动计划和控制以及执行器引起的其他不可避免的延迟的计算延迟被系统地和统一解决。为了处理所有这些延迟,在我们的框架中:1)我们提出了一种新的过滤方法,而没有事先了解动态和干扰分布的知识,以适应,安全地估算时间变化的计算延迟; 2)我们为转向延迟建模驱动动力学; 3)所有约束优化均在强大的管模型预测控制器中实现。对于应用的优点,我们证明我们的方法适合自动驾驶和自动赛车。我们的方法是独立延迟补偿控制器的新型设计。此外,在假设无延迟作为主要控制器的学习控制器的情况下,我们的方法是主要控制器的安全保护器。
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路径规划是自治车辆运动规划中的关键组成部分。路径指定车辆将旅行的几何形状,因此,对安全和舒适的车辆运动至关重要。对于城市驾驶场景,自治车辆需要能够在杂乱的环境中导航,例如,道路部分被侧面挡住的车辆/障碍物。如何生成运动学上可行和平滑的路径,可以避免复杂环境中的碰撞,使路径规划有挑战性的问题。在本文中,我们提出了一种新型二次编程方法,可以产生分辨率完全碰撞避免能力的最佳路径。
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历史上,轨迹计划和控制已分为自动驾驶堆栈中的两个模块。轨迹计划的重点是更高级别的任务,例如避免障碍物并保持在路面上,而控制器则尽最大努力遵循有史以来不断变化的参考轨迹。我们认为,由于计划中的轨迹与控制器可以执行的内容不匹配,因此这种分离是有缺陷的,并且(2)由于模型预测性控制(MPC)范式的灵活性而不必要。取而代之的是,在本文中,我们提出了一个基于统一的MPC轨迹计划和控制计划,该计划可确保在道路边界,静态和动态环境方面的可行性,并实施乘客舒适性限制。在各种方案中,对该方案进行了严格的评估,这些方案旨在证明最佳控制问题(OCP)设计和实时解决方案方法的有效性。原型代码将在https://github.com/watonomous/control上发布。
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Motion planning is challenging for autonomous systems in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this paper, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in simulation and real world setting. Experimental results show that the proposed methods can generate smooth collision-free trajectories with less computation time compared with other benchmarks and perform robustly in cluttered environments.
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本文提出了一种新的规划和控制策略,用于赛车场景中的多辆车竞争。所提出的赛车策略在两种模式之间切换。当没有周围的车辆时,使用基于学习的模型预测控制(MPC)轨迹策划器用于保证自助车辆更好地实现了更好的搭接定时。当EGO车辆与其他围绕车辆竞争以超车时,基于优化的策划器通过并行计算产生多个动态可行的轨迹。每个轨迹在MPC配方下进行优化,其具有不同的同型贝塞尔曲线参考路径,横向于周围的车辆之间。选择这些不同的同型轨迹之间的时间最佳轨迹,并使用具有障碍物避免约束的低级MPC控制器来保证系统的安全性能。所提出的算法具有能够生成无碰撞轨迹并跟踪它们,同时提高杠杆定时性能,稳定的低计算复杂性,优于汽车赛车环境的时序和性能中的现有方法。为了展示我们的赛车策略的表现,我们在轨道上模拟了多个随机生成的移动车辆,并测试自我车辆的超越机动。
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Designing a local planner to control tractor-trailer vehicles in forward and backward maneuvering is a challenging control problem in the research community of autonomous driving systems. Considering a critical situation in the stability of tractor-trailer systems, a practical and novel approach is presented to design a non-linear MPC(NMPC) local planner for tractor-trailer autonomous vehicles in both forward and backward maneuvering. The tractor velocity and steering angle are considered to be control variables. The proposed NMPC local planner is designed to handle jackknife situations, avoiding multiple static obstacles, and path following in both forward and backward maneuvering. The challenges mentioned above are converted into a constrained problem that can be handled simultaneously by the proposed NMPC local planner. The direct multiple shooting approach is used to convert the optimal control problem(OCP) into a non-linear programming problem(NLP) that IPOPT solvers can solve in CasADi. The controller performance is evaluated through different backup and forward maneuvering scenarios in the Gazebo simulation environment in real-time. It achieves asymptotic stability in avoiding static obstacles and accurate tracking performance while respecting path constraints. Finally, the proposed NMPC local planner is integrated with an open-source autonomous driving software stack called AutowareAi.
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在本文中,我们提出了针对无人接地车辆(UGV)的新的控制屏障功能(CBF),该功能有助于避免与运动学(非零速度)障碍物发生冲突。尽管当前的CBF形式已经成功地保证了与静态障碍物的安全/碰撞避免安全性,但动态案例的扩展已获得有限的成功。此外,借助UGV模型,例如Unicycle或自行车,现有CBF的应用在控制方面是保守的,即在某些情况下不可能进行转向/推力控制。从经典的碰撞锥中汲取灵感来避免轨迹规划,我们介绍了其新颖的CBF配方,并具有对独轮车和自行车模型的安全性保证。主要思想是确保障碍物的速度W.R.T.车辆总是指向车辆。因此,我们构建了一个约束,该约束确保速度向量始终避开指向车辆的向量锥。这种新控制方法的功效在哥白尼移动机器人上进行了实验验证。我们将其进一步扩展到以自行车模型的形式扩展到自动驾驶汽车,并在Carla模拟器中的各种情况下证明了避免碰撞。
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具有许多移动代理的城市环境的运动计划可以看作是组合问题。通过在左右之后,左右或左后通过障碍物,自动驾驶汽车可以选择执行多个选项。这些组合方面需要在计划框架中考虑到。我们通过提出一种结合轨迹计划和操纵推理的新型计划方法来解决这个问题。我们定义了沿参考曲线的动态障碍的分类,使我们能够提取战术决策序列。我们将纵向和横向运动分开,以加快基于优化的轨迹计划。为了将获得的轨迹集绘制为操纵变体,我们定义了一种语义来描述它们。这使我们能够选择最佳轨迹,同时还可以确保随着时间的推移操纵的一致性。我们证明了我们的方法的能力,即仍被普遍认为是具有挑战性的场景。
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随着使用复杂非线性优化但计算资源有限的经济实惠的自动驾驶车辆,计算时间成为关注问题。其他因素,如执行器动力学和执行器命令处理成本也不可避免地导致延迟。在高速场景中,这些延迟对于车辆的安全至关重要。最近的作品将这些延迟单独考虑,但没有在自动驾驶的背景下统一它们。此外,最近的作品不恰当地考虑计算时间作为恒定或大的上限,这使得控制较少响应或过保守。要处理所有这些延迟,我们通过1)统一的框架,使用鲁棒管模型预测控制,3)使用新型Adaptive Kalman滤波器,无需假定已知的过程模型和噪声协方差,这使得控制器安全尽量减少保守性。在一次性的情况下,我们的方法可以作为独立控制器;在其他手上,我们的方法为高级控制器提供了一个安全防护装置,这不拖延。这可以用于在部署在简单环境中培训的黑匣子学习的控制器时补偿SIM-TO-REAL间隙,而不考虑实际车辆系统的延迟。
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Although extensive research in planning has been carried out for normal scenarios, path planning in emergencies has not been thoroughly explored, especially when vehicles move at a higher speed and have less space for avoiding a collision. For emergency collision avoidance, the controller should have the ability to deal with complicated environments and take collision mitigation into consideration since the problem may have no feasible solution. We propose a safety controller by using model predictive control and artificial potential function. A new artificial potential function inspired by line charge is proposed as the cost function for our model predictive controller. The new artificial potential function takes the shape of all objects into consideration. In particular, the artificial potential function that we proposed has the flexibility to fit the shape of the road structures such as the intersection, while the artificial potential function in most of the previous work could only be used in a highway scenario. Moreover, we could realize collision mitigation for a specific part of the vehicle by increasing the quantity of the charge at the corresponding place. We have tested our methods in 192 cases from 8 different scenarios in simulation. The simulation results show that the success rate of the proposed safety controller is 20% higher than using HJ-reachability with system decomposition. It could also decrease 43% of collision that happens at the pre-assigned part.
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在本文中,我们在局部不同的牵引条件下解决了处理限制的运动规划和控制问题。我们提出了一种新的解决方案方法,其中通过源自预测摩擦估计来表示预测地平线上的牵引变化。在后退地平线时装解决了约束的有限时间最佳控制问题,施加了这些时变的约束。此外,我们的方法具有集成的采样增强程序,该过程解决了对突然约束改变而产生的局部最小值的不可行性和敏感性的问题,例如,由于突然的摩擦变化。我们在一系列临界情景中验证了沃尔沃FH16重型车辆的提议算法。实验结果表明,通过确保计划运动的动态可行性,通过确保高牵引利用时,牵引自适应运动规划和控制改善了避免事故的车辆的能力,既通过适应低局部牵引。
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我们为非全面移动机器人设计了MPC方法,并在分析上表明,随着时间的变化,线性化的系统可以在跟踪任务中的来源周围产生渐近稳定性。为了避免障碍物,我们提出了速度空间中的约束,该约束根据当前状态明确耦合两个控件输入。
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本文介绍了一种新的方法,为入境驾驶场景的自动车辆产生最佳轨迹。该方法使用两相优化过程计算轨迹。在第一阶段中,优化过程产生具有不同的曲率的闭形驾驶导向线。在第二阶段,该过程将驱动导向线作为输入输出,输出沿着导向线驾驶的车辆的动态可行,混蛋和时间最佳轨迹。该方法对于在弯曲道路上产生轨迹特别有用,其中车辆需要频繁加速和减速以适应离心机加速限制。
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在本文中,我们提出了一种反应性约束导航方案,并避免了无人驾驶汽车(UAV)的嵌入式障碍物,以便在障碍物密集的环境中实现导航。拟议的导航体系结构基于非线性模型预测控制(NMPC),并利用板载2D激光雷达来检测障碍物并在线转换环境的关键几何信息为NMPC的参数约束,以限制可用位置空间的可用位置空间无人机。本文还重点介绍了所提出的反应导航方案的现实实施和实验验证,并将其应用于多个具有挑战性的实验室实验中,我们还与相关的反应性障碍物避免方法进行了比较。提出的方法中使用的求解器是优化引擎(开放)和近端平均牛顿进行最佳控制(PANOC)算法,其中采用了惩罚方法来正确考虑导航任务期间的障碍和输入约束。拟议的新颖方案允许快速解决方案,同时使用有限的车载计算能力,这是无人机的整体闭环性能的必需功能,并在多个实时场景中应用。内置障碍物避免和实时适用性的结合使所提出的反应性约束导航方案成为无人机的优雅框架,能够执行快速的非线性控制,本地路径计划和避免障碍物,所有框架都嵌入了控制层中。
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Autonomous driving has a natural bi-level structure. The goal of the upper behavioural layer is to provide appropriate lane change, speeding up, and braking decisions to optimize a given driving task. However, this layer can only indirectly influence the driving efficiency through the lower-level trajectory planner, which takes in the behavioural inputs to produce motion commands. Existing sampling-based approaches do not fully exploit the strong coupling between the behavioural and planning layer. On the other hand, end-to-end Reinforcement Learning (RL) can learn a behavioural layer while incorporating feedback from the lower-level planner. However, purely data-driven approaches often fail in safety metrics in unseen environments. This paper presents a novel alternative; a parameterized bi-level optimization that jointly computes the optimal behavioural decisions and the resulting downstream trajectory. Our approach runs in real-time using a custom GPU-accelerated batch optimizer, and a Conditional Variational Autoencoder learnt warm-start strategy. Extensive simulations show that our approach outperforms state-of-the-art model predictive control and RL approaches in terms of collision rate while being competitive in driving efficiency.
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