GT-AE researchers Panagiotis Tsiotras and Evangelos Theodorou have been workiing with faculty from the School of Interactive Computing (IC) to devise a novel way for the self-driving cars of tomorrow to drive safely under actual road conditions.
The team has been quietly testing its work on the Georgia Tech Autonomous Racing Facility on Marietta Street for the last few months, using one-fifth-scale, fully autonomous auto-rally cars that operate at the equivalent of 90 mph.
Sponsored by the U.S. Army Research Office, the research seeks to increase vehicular stability while maintaining performance. Their technique – which uses advanced algorithms, onboard computing, and specially installed sensing devices – is garnering some serious attention, too.
Last month, it was presented at the International Conference on Robotics and Automation (ICRA). In the days that followed, it was celebrated in a number of online media outlets, all of them eager to showcase the next big technological breakthrough in driverless vehicles. (This You-Tube video received more than 80K views.)
“An autonomous vehicle should be able to handle any condition, not just drive on the highway under normal conditions,” said Tsiotras, an expert on the mathematics behind rally-car racing control.
“One of our principal goals is to infuse some of the expert techniques of human drivers into the brains of these autonomous vehicles.”
Traditional robotic-vehicle techniques use the same control approach whether a vehicle is driving normally or at the edge of roadway adhesion, Tsiotras explained. The Georgia Tech method – known as model predictive path integral control (MPPI) – was developed specifically to address the non-linear dynamics involved in controlling a vehicle near its friction limits.
Utilizing Advanced Concepts
“Aggressive driving in a robotic vehicle – maneuvering at the edge – is a unique control problem involving a highly complex system,” said Evangelos Theodorou, an AE assistant professor who is leading the project. “However, by merging statistical physics with control theory, and utilizing leading-edge computation, we can create a new perspective, a new framework, for control of autonomous systems.”
The Georgia Tech researchers used a stochastic trajectory-optimization capability, based on a path-integral approach, to create their MPPI control algorithm, Theodorou explained. Using statistical methods, the team integrated large amounts of handling-related information, together with data on the dynamics of the vehicular system, to compute the most stable trajectories from myriad possibilities.
Processed by the high-power graphics processing unit (GPU) that the vehicle carries, the MPPI control algorithm continuously samples data coming from global positioning system (GPS) hardware, inertial motion sensors, and other sensors. The onboard hardware-software system performs real-time analysis of a vast number of possible trajectories and relays optimal handling decisions to the vehicle moment by moment.
In essence, the MPPI approach combines both the planning and execution of optimized handling decisions into a single highly efficient phase. It’s regarded as the first technology to carry out this computationally demanding task; in the past, optimal- control data inputs could not be processed in real time.
Fully Autonomous Vehicles
The researchers’ two auto-rally vehicles – custom built by the team – utilize special electric motors to achieve the right balance between weight and power. The cars carry a motherboard with a quad-core processor, a potent GPU, and a battery.
Each vehicle also has two forward-facing cameras, an inertial measurement unit, and a GPS receiver, along with sophisticated wheel-speed sensors. The power, navigation, and computation equipment is housed in a rugged aluminum enclosure able to withstand violent rollovers. Each vehicle weighs about 48 pounds and is about three feet long.
These rolling robots are able to test the team’s control algorithms without any need for off-vehicle devices or computation, except for a nearby GPS receiver. The onboard GPU lets the MPPI algorithm sample more than 2,500, 2.5-second-long trajectories in under 1/60 of a second.
An important aspect in the team’s autonomous-control approach centers on the concept of “costs” – key elements of system functionality. Several cost components must be carefully matched to achieve optimal performance.
In the case of the Georgia Tech vehicles, the costs consist of three main areas: the cost for staying on the track, the cost for achieving a desired velocity, and the cost of the control system.
A sideslip-angle cost was also added to improve vehicle stability.
The cost approach is important to enabling a robotic vehicle to maximize speed while staying under control, explained James Rehg, a professor in the Georgia Tech School of Interactive Computing who is collaborating with Theodorou and Tsiotras.
It’s a complex balancing act, Rehg said. For example, when the researchers reduced one cost term to try to prevent vehicle sliding, they found they got increased drifting behavior.
“What we're talking about here is using the MPPI algorithm to achieve relative
entropy minimization – and adjusting costs in the most effective way is a big part of that,” he said. “To achieve the optimal combination of control and performance in an autonomous vehicle is definitely a non-trivial problem.
Story courtesy of Rick Robinson, Georgia Tech Research News