Clothoid-Based Global Path Planning for Autonomous Vehicles in Urban Scenarios

Sell

Journal of Field Robotics

et al. 2022

This paper proposes a reliable optimized sigmoid‐based path planning algorithm that ensures smooth, fast and safe overtaking maneuver, while maintaining the necessary safety distance. In the proposed method, the desired smoothness of trajectories, the changes in steering angle and the lateral acceleration are controlled in a robust way. This paper describes the simulations, and the confirming real‐world experiments, conducted using the autonomous shuttle iseAuto. Our results suggest that the sigmoid A‐star algorithm leads to a smoother and more reliable motion when compared to other two standard methods. Specifically, the abruptness of necessary steering angle changes is reduced by factor of 4, and approaching the level of an experienced driver‐like maneuver.

Section: Related Workmentioning

confidence: 99%

Practical path planning techniques in overtaking for autonomous shuttles

Sell

Journal of Field Robotics

et al. 2022

Robotics: Science and Systems XVII

“…The Clothoid representation -also known as Euler curves, has some inherent properties such as (1) linearly varying curvature, and (2) a compact representation (small number of parameters), that will allow us to realize online adaptation within our optimization framework. Clothoids have been utilized in road design [20], path [5] and attitude [13] planning, autonomous driving [19,29], and continuum robotics [14].…”

Section: A Model Representationmentioning

confidence: 99%

Skill-based Shared Control

Mower¹,

Moura²,

Vijayakumar³

2021

Performing a number of motion patterns -referred to as skills -(e.g., wave, spiral, sweeping motions) during teleoperation is an integral part of many industrial processes such as spraying, welding, and wiping (cleaning, polishing). Maintaining these motions whilst simultaneously avoiding obstacles and traversing complex terrain requires expert operators. In this work, we propose a novel skill-based shared control framework for incorporating the notion of skill assistance to aid novice operators to sustain these motion patterns whilst adhering to environmental constraints. Our shared control method uses streaming joystick data to estimate the model parameters that provide a description of the operator's intention. We introduce a novel parametrization for state and control that combines skill and underlying trajectory models, leveraging a special type of curve known as Clothoids. This new parameterization allows for efficient computation of skill-based short term horizon plans, enabling the use of a Model Predictive Control (MPC) loop. We perform experiments on a hardware mock-up, validating the effectiveness of our method to recognize a switch of intended skill, and showing an improved quality of output motion, even under dynamically changing obstacles. See our accompanying video here: https://youtu.be/TwhsgA6fw6M.

“…Clothoid paths [20] address feasibility issues by placing constraints on both curvature and curvature rate over the path resulting in paths with piece-wise constant curvature rate. A clothoid, or Euler Spiral, is a curve whose instantaneous curvature, κ is a linear function of the curve's arc-length, s. When the start and goal configurations are given as an (x, y) pose, a heading θ, and a curvature κ, several methods have been proposed to generate the shortest path from start to goal as a sequence of clothoid and straight line segments [8], [21], [22]. However, since the curvature of a clothoid varies linearly with arc length, the sharpness of the path (and thus the lateral jerk) is constant.…”

Section: B Related Workmentioning

confidence: 99%

Tunable Trajectory Planner Using G3 Curves

Botros¹,

Smith²

2021

Preprint

Trajectory planning is commonly used as part of a local planner in autonomous driving. This paper considers the problem of planning a continuous-curvature-rate trajectory between fixed start and goal states that minimizes a tunable trade-off between passenger comfort and travel time. The problem is an instance of infinite dimensional optimization over two continuous functions: a path, and a velocity profile. We propose a simplification of this problem that facilitates the discretization of both functions. This paper also proposes a method to quickly generate minimal-length paths between start and goal states based on a single tuning parameter: the second derivative of curvature. Furthermore, we discretize the set of velocity profiles along a given path into a selection of acceleration way-points along the path. Gradient-descent is then employed to minimize cost over feasible choices of the second derivative of curvature, and acceleration way-points, resulting in a method that repeatedly solves the path and velocity profiles in an iterative fashion. Numerical examples are provided to illustrate the benefits of the proposed methods.