DeepAI AI Chat
Log In Sign Up

Autonomous Parking by Successive Convexification and Compound State Triggers

by   Ali Boyali, et al.

In this paper, we propose an algorithm for optimal generation of nonholonomic paths for planning parking maneuvers with a kinematic car model. We demonstrate the use of Successive Convexification algorithms (SCvx), which guarantee path feasibility and constraint satisfaction, for parking scenarios. In addition, we formulate obstacle avoidance with state-triggered constraints which enables the use of logical constraints in a continuous formulation of optimization problems. This paper contributes to the optimal nonholonomic path planning literature by demonstrating the use of SCvx and state-triggered constraints which allows the formulation of the parking problem as a single optimisation problem. The resulting algorithm can be used to plan constrained paths with cusp points in narrow parking environments.


page 1

page 2

page 3

page 4


Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation

We present a novel approach to path planning for robotic manipulators, i...

Path Planning with Kinematic Constraints for Robot Groups

Path planning for multiple robots is well studied in the AI and robotics...

Multi-Robot Path Planning in Complex Environments via Graph Embedding

We propose an approach to solve multi-agent path planning (MPP) problems...

Inferring Obstacles and Path Validity from Visibility-Constrained Demonstrations

Many methods in learning from demonstration assume that the demonstrator...

Reflected Schrödinger Bridge: Density Control with Path Constraints

How to steer a given joint state probability density function to another...

Optimal Needle Diameter, Shape, and Path in Autonomous Suturing

Needle shape, diameter, and path are critical parameters that directly a...

Learning from Experience for Rapid Generation of Local Car Maneuvers

Being able to rapidly respond to the changing scenes and traffic situati...