Optimal motion planning with the half-car dynamical model for autonomous high-speed driving

Jeon, Jeong hwan; Cowlagi, Raghvendra V.; Peters, Steven; Karaman, Sertaç; Frazzoli, Emilio; Tsiotras, Panagiotis; Iagnemma, Karl

doi:10.1109/acc.2013.6579835

Cited by 68 publications

(42 citation statements)

References 18 publications

(48 reference statements)

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“…This model works well for low speeds or less aggressive driving behaviors. 2) Combined Slip Model with Load Transfer: Combined breaking and steering is one of the most essential aspects of vehicle safety and motivates our choice for a combined slip model [31]. Specifically, we allow load transfer between the front and rear tires -a technique often used by rally racing drivers to control the yaw dynamics [32].…”

Section: Motion Modelsmentioning

confidence: 99%

“…µ α,max is the maximum allowed friction coefficient. The constraint essentially limits the yaw rate dependent on the state z, prohibiting unsafe driving modes suitable even for race-car driving under significant amounts of slip [31]. The constraints on steering speed |δ| ≤δ max , steering angle |δ| ≤ δ max , longitudinal speed, v x ≤ v x,max , remain the same with an additional constraint on the lateral velocity |v y | ≤ v y,max .…”

Section: Motion Modelsmentioning

confidence: 99%

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Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

Schwarting

Alonso–Mora

Paull

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-High-end vehicles are already equipped with safety systems, such as assistive braking and automatic lane following, enhancing vehicle safety. Yet, these current solutions can only help in low-complexity driving situations. In this work, we introduce a Parallel Autonomy, or shared control, framework that computes safe trajectories for an automated vehicle, based on human inputs. We minimize the deviation from the human inputs while ensuring safety via a set of collision avoidance constraints. Our method achieves safe motion even in complex driving scenarios, such as those commonly encountered in an urban setting. We introduce a receding horizon planner formulated as Nonlinear Model Predictive Control (NMPC), which includes analytic descriptions of road boundaries and the configuration and future uncertainties of other road participants. The NMPC operates over both steering and acceleration simultaneously. We introduce a nonslip model suitable for handling complex environments with dynamic obstacles, and a nonlinear combined slip vehicle model including normal load transfer capable of handling static environments. We validate the proposed approach in two complex driving scenarios. First, in an urban environment that includes a left-turn across traffic and passing on a busy street. And second, under snow conditions on a race track with sharp turns and under complex dynamic constraints. We evaluate the performance of the method with various human driving styles. We consequently observe that the method successfully avoids collisions and generates motions with minimal intervention for Parallel Autonomy. We note that the method can also be applied to generate safe motion for fully autonomous vehicles.

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Section: Motion Modelsmentioning

confidence: 99%

Section: Motion Modelsmentioning

confidence: 99%

Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

Schwarting

Alonso–Mora

Paull

et al. 2018

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…RRTs have also explicitly addressed differential constraints [7]- [9], but equations of motion, which cannot be neglected when considering, for example, nonholonomic or automotive systems, are not trivially incorporated into the traditional RRT framework. To connect two nodes is a twopoint boundary value problem, and in general there are no guarantees that a solution exists [10].…”

Section: Introductionmentioning

confidence: 99%

Particle filtering for online motion planning with task specifications

Berntorp

Cairano

2016

2016 American Control Conference (ACC)

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A probabilistic framework for online motion planning of vehicles in dynamic environments is proposed. We develop a sampling-based motion planner that incorporates prediction of obstacle motion. A key feature is the introduction of task specifications as artificial measurements. This allows us to cast the exploration phase in the planner as a nonlinear, possibly multimodal, estimation problem, which is effectively solved using particle filtering. For certain parameter choices, the approach is equivalent to solving a nonlinear estimation problem using particle filtering. The proposed approach is illustrated on a simulated autonomous-driving example. The results indicate that our method is computationally efficient, consistent with the task specifications, and computes dynamically feasible trajectories.

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“…Their controller-driven variants are usually obtained by integration of a specific extend procedure into the planner. Utilized extend procedures range from general optimal-control approches such as [40], through spline path segments (e.g., [18,54]), to more controller-driven approaches such as simulation of mobile robot with closed-loop control system proposed in [38] and control-Lyapunov function approach from [39]. Sampling-based algorithms are attractive due to their generality and ability to cope with virtually any robot and motion environment model.…”

Section: Related Workmentioning

confidence: 99%

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

Gawron

Michałek

2017

J Intell Robot Syst

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Integrated motion planning and control for the purposes of maneuvering mobile robots under state-and input constraints is a problem of vital practical importance in applications of mobile robots such as autonomous transportation. Those constraints arise naturally in practice due to specifics of robot mechanical construction and the presence of obstacles in motion environment. In contrast to approaches focusing on feedback control design under the assumption of given reference motion or motion planning with neglection of subsequent feedback motion execution, we adopt a controller-driven motion planning paradigm, which has recently gained attention of many researchers. It postulates design of motion planning algorithms dedicated to specific feedback control policies, which compute a sequence of feedback control subtasks instead of classically planned open-loop controls or parametric paths. In this spirit, we propose a motion planning algorithm driven by the VFO (Vector Field Orientation) control law for the waypointfollowing task. Presented analysis of the VFO control law reveals its beneficial properties, which are subsequently utilized to solve a generally nonlinear and non-convex optimal motion planning problem by formulating it as a mixed-integer linear program (MILP). The solution proposed in this paper yields a waypoint sequence, which is designed for execution by application of the VFO control law to drive a robot to a prescribed final configuration under an input constraint imposed by bounded curvature of robot motion and state constraints resulting from a convex decomposition of task space. Satisfaction of these constraints is guaranteed analytically and exactly, i.e., without utilization of numerical approximations. Moreover, for a given discrete set of possible waypoint orientations, the proposed algorithm computes plans optimal w.r.t. given cost functional, which can be any convex linear combination of quantities such as robot path length, curvature of robot motion, distance to imposed state constraints, etc. Furthermore, the planning algorithm exploits the possibility of both forward or backward movement of the robot to allow maneuvering in demanding environments. Generated waypoint sequences are a compact representation of a motion plan, which can be immediately executed with the VFO controller without any additional postprocessing. Validity of the proposed approach has 266 J Intell Robot Syst (2018) 89:265-297 been confirmed by simulation studies and experimental motion execution with a laboratory-scale mobile robot.

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Optimal motion planning with the half-car dynamical model for autonomous high-speed driving

Cited by 68 publications

References 18 publications

Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

Safe Nonlinear Trajectory Generation for Parallel Autonomy With a Dynamic Vehicle Model

Particle filtering for online motion planning with task specifications

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

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