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09.06.2017

Path Planning and Control Solutions for Autonomous Vehicles

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While autonomous vehicles are gaining usage across numerous industries, the concept of self-navigation raises many questions, and rightfully so. With no human in direct control of the vehicle to navigate where to go, how to get there, or make real-time decisions along the way, how do autonomous vehicles actually determine their paths?

Accurate and reliable detection of obstacles is paramount to collision avoidance, but can be quite challenging as obstructions tend to vary in size and geometry. Some objects, such as dust, can cause a false-positive or false-negative reaction, when in reality, there is no impending collision.

ASI’s advanced Path Planning and Control makes it all possible. By utilizing state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML), ASI has developed precision control algorithms that enable autonomous vehicles to accomplish their tasks safely and efficiently.

 

Path Planning and Control
The basic framework of Path Planning and Control starts with programming an objective for the autonomous vehicle to achieve. To accomplish this task, the machine must choose a path and adjust to obstacles, terrain, and changing conditions in order to reach its destination safely.

By utilizing a primarily LiDAR-based perception system, a cost surface is created as an input to a nonlinear optimization solver. The current vehicle state, desired vehicle state, perceived-cost surface, vehicle dynamics, and vehicle kinematics are vital inputs the solver uses to generate feasible path options for the vehicle.

 

Autonomous Navigation
ASI’s AI algorithms are then used to facilitate safe and reliable navigation of unknown or dangerous terrain to arrive at the desired location. With this terrain model, the vehicle is able to predict future behaviors for hazard avoidance and optimal trajectory selection.



World Modeling
Exteroceptive sensors on the vehicle collect data from the surroundings, which are processed by the AI algorithm for sensor fusion to build a highly accurate model of the environment. This model can be accessed and updated by any autonomous vehicle entering the area.

Leader Follower Operation
Another feature of ASI’s autonomous vehicles is the ability to follow the leader. Whether the leader be a manned vehicle, unmanned vehicle, or even a person on foot, this ASI algorithm enables autonomous vehicles to follow the defined leader while still maintaining their individual collision avoidance and terrain navigation features.

Machine Learning
ASI’s machine learning is another important part of their autonomous vehicle solutions. Here are some of the ML capabilities:

 

  • Auto Tuning – By monitoring and comparing actual and desired behaviors, parameters are automatically adjusted for continuous improvement.
  • Obstacle Identification and Classification – ASI researchers are improving world modeling and object learning that will then be shared between vehicles for collaborative and improved behaviors.
  • Convoy Operations – ASI is also developing convoy technology that will increase the efficiency of vehicle groups through unknown and harsh terrain.

Leader Follower Operation
Another feature of ASI’s autonomous vehicles is the ability to follow the leader. Whether the leader be a manned vehicle, unmanned vehicle, or even a person on foot, this ASI algorithm enables autonomous vehicles to follow the defined leader while still maintaining their individual collision avoidance and terrain navigation features.

Path Planning and Control are essential features for vehicle automation made possible by ASI’s innovative AI and ML technology. As the world leader in vendor independent vehicle automation systems, Autonomous Solutions, Inc. delivers automation solutions to the mining, agriculture, automotive, government, and manufacturing industries with unrivaled performance.

Learn more about our advanced autonomous vehicle solutions today!