Local Path Control for an Autonomous Vehicle

1990 ◽  
pp. 38-44 ◽  
Author(s):  
Winston L. Nelson ◽  
Ingemar J. Cox
Keyword(s):  
Autonomous Vehicle ◽  
Path Control ◽  
Local Path

Autonomous Vehicle Trajectory Planning under Uncertainty Using Stochastic Collocation

2012 ◽  
Vol 580 ◽  
pp. 175-179 ◽  
Author(s):  
Hong Fu Liu ◽  
Yu Zhang ◽  
Shao Fei Chen ◽  
Jing Chen
Keyword(s):  
Trajectory Planning ◽  
Autonomous Vehicle ◽  
Stochastic Collocation ◽  
Planning Under Uncertainty ◽  
Boundary Constraints ◽  
Probabilistic Uncertainty ◽  
Path Control ◽  
Vehicle Trajectory ◽  
Planning Problems ◽  
Optimal Trajectory Planning

We propose a framework based on stochastic collocation to solve autonomous vehicle optimal trajectory planning problems with probabilistic uncertainty. We model uncertainty from the location and size of obstacles. We develop stochastic pseudospectral methods to solve the minimum expectation cost of differential equation, which meets path, control, and boundary constraints. Results are shown on two examples of autonomous vehicle trajectory planning under uncertainty, which illustrated the feasibility and applicability of our method.

scholarly journals Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5547
Author(s):  
Younes Al Younes ◽  
Martin Barczyk
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Real Time ◽  
Nonlinear Model ◽  
Feedback Linearization ◽  
Autonomous Vehicle ◽  
Flight Test ◽  
Local Path Planning ◽  
Planning Approach ◽  
Local Path

Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.

scholarly journals Incentivizing Stable Path Selection in Future Internet Architectures

2021 ◽  
Vol 48 (3) ◽  
pp. 12-13
Author(s):  
Simon Scherrer ◽  
Markus Legner ◽  
Adrian Perrig ◽  
Stefan Schmid
Keyword(s):  
Incentive Compatibility ◽  
Future Internet ◽  
Path Selection ◽  
Selection Strategy ◽  
Theoretic Model ◽  
Path Control ◽  
Stable Path ◽  
Selection Policies ◽  
Future Internet Architectures ◽  
Local Path

By delegating path control to end-hosts, future Internet architectures offer flexibility for path selection. However, a concern arises that the distributed routing decisions by endhosts, in particular load-adaptive routing, can lead to oscillations if path selection is performed without coordination or accurate load information. Prior research has addressed this problem by devising local path-selection policies that lead to global stability. However, little is known about the viability of these policies in the Internet context, where selfish end-hosts can deviate from a prescribed policy if such a deviation is beneficial from their individual perspective. In order to achieve network stability in future Internet architectures, it is essential that end-hosts have an incentive to adopt a stability-oriented path-selection policy. In this work, we perform the first incentive analysis of the stability-inducing path-selection policies proposed in the literature. Building on a game-theoretic model of end-host path selection, we show that these policies are in fact incompatible with the self-interest of end-hosts, as these strategies make it worthwhile to pursue an oscillatory path-selection strategy. Therefore, stability in networks with selfish endhosts must be enforced by incentive-compatible mechanisms. We present two such mechanisms and formally prove their incentive compatibility.

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