Fast Trajectory Optimization

Fast Trajectory Optimization

Trajectory optimization is a powerful tool for motion planning, enabling the synthesis of dynamic motion for complex underactuated robotic systems. This general framework can be applied to robots with nonlinear dynamics and constraints where other motion planning paradigms—such as sample-based planning, inverse dynamics, or differential flatness—are impractical or ineffective.

In early 2019, we released the first iteration of the ALTRO algorithm (Augmented Lagrangian TRajectory Optimizer), which combines the robustness, versatility, and generality of direct methods such as DIRCOL with the speed of indirect methods like iterative LQR (iLQR). The core of the algorithm applies iLQR within an Augmented Lagrangian outer loop that handles general inequality and equality constraints, including obstacle avoidance. Novel refinements include improved numerical conditioning, minimum time formulations, the ability to feed a dynamically infeasible state trajectory as an initial guess, and anytime feasibility through using projected Newton steps. The result is a state-of-the-art trajectory optimization solver that has already been applied to a variety of problems with some promising results.

In 2020, ALTRO was split into several core packages, including RobotDynamics.jl, TrajectoryOptimization.jl, and Altro.jl, and was optimized for performance. In late 2020, we released ALTRO-C, a modification that included the ability to handle second-order cone programs (SOCPs) and demonstrated state-of-the-art performance against both OSQP and ECOS.

ALTRO also has the ability to plan directly on the space of 3D rotations by optimizing on the Lie Algebra while representing the trajectory using unit quaternions.

ALTRO running a quadcopter through a maze Quadrotor Flip

drawing

Related Papers

2021
May
PDF Planning with Attitude
Brian Jackson, Kevin Tracy, and Zac Manchester
Robotics Automation and Letters (RAL). (Accepted)
2021
May
PDF ALTRO-C: A Fast Solver for Conic Model-Predictive Control
Brian Jackson, Tarun Punnoose, Daniel Neamati, Kevin Tracy, Rianna Jitosho, and Zac Manchester
International Conference on Robotics and Automation (ICRA). (Submitted)
2020
May
PDF Scalable Cooperative Transport of Cable-Suspended Loads with UAV's using Distributed Trajectory Optimization
Brian Jackson, Taylor Howell, Kunal Shah, Mac Schwager, and Zac Manchester
International Conference on Robotics and Automation (ICRA). Paris, France.
2019
November
PDF ALTRO: A Fast Solver for Constrained Trajectory Optimization
Taylor Howell, Brian Jackson, and Zac Manchester
International Conference on Intelligent Robots and Systems (IROS). Macao, China.
2017
September
PDF Constrained Unscented Dynamic Programming
Brian Plancher, Zac Manchester, and Scott Kuindersma
International Conference on Intelligent Robots and Systems (IROS). Vancouver, Canada.

People

Zac Manchester
Assistant Professor
Taylor Howell
Robust feedback motion planning and optimization through contact
Brian Jackson
Real-time motion planning
Andrew Gatherer
Improving small-satellites
Last updated: 2019-12-04