Agile and intelligent robots

Agile and Intelligent Robots

Developing novel controls for robotic systems operating safely in complex environments

Our Contribution

Researchers in APL’s Intelligent Systems Center (ISC) work to create robots that can reason intelligently about the world in order to operate autonomously and safely, at high speeds, in the wild.

Model-Based Control

Fixed-wing uncrewed aerial vehicles (UAVs) outperform their rotary-wing counterparts in speed, range, and endurance—but are severely limited with regard to agility. ISC researchers believe this limitation is due less to physical characteristics than to the inability of the vehicle’s control system to effectively reason about complex nonlinear dynamics and flow regimes.

To improve the agility of fixed-wing UAVs, a team from the ISC developed a nonlinear model predictive control (NMPC) algorithm capable of running in real time and exploiting the full nonlinear post-stall regime of the aircraft. Post-stall flight affords unique opportunities for increasing vehicle agility by minimizing the required deceleration distance and dramatically reducing the minimum turning radius.

Fixed-Wing Navigation in Constrained Environments

To test the NMPC algorithm, ISC researchers explored the receding-horizon case of navigating through a narrow corridor. They evaluated the algorithm in both simulation and hardware on a 24-inch-wingspan UAV, demonstrating agile fixed-wing flight using offboard sensing and computation.

This approach was also tested using onboard sensing and computation on a 42-inch-wingspan UAV operating in an urban environment.

Related Publications

  • Basescu, Max, and Joseph Moore. "Direct NMPC for post-stall motion planning with fixed-wing uavs." 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2020).
  • Max Basescu, Adam Polevoy, Bryanna Yeh, Luca Scheuer, Erin Sutton, Joseph L. Moore, "Agile Fixed-Wing UAVs for Urban Swarm Operations," Field Robotics.

Dynamic Wing-Morphing Control

APL researchers demonstrated the improved performance in post-stall maneuver planning enabled by unsteady aerodynamics within a UAV’s flight dynamics model. A vortex particle model of local aerodynamics is integrated into model-based control for a morphing-wing UAV. This UAV’s performance is compared for gliding perch maneuvers with and without wing morphing and unsteady aerodynamics. Wing morphing planned using quasi-steady aerodynamics dramatically reduced performance, but wing morphing planned with unsteady aerodynamics improved performance. However, this result required pre-planning time; unsteady solutions generated in flight did not converge quickly enough for improved control.

Related Publications

  • Gino Perrotta, Luca Scheuer, Yocheved Kopel, Max Basescu, Adam Polevoy, Kevin Wolfe, Joseph Moore, “Path Planning for a Morphing-Wing UAV Using a Vortex Particle Model,” 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023).

Precision Post-Stall Landing

The second domain ISC researchers explored was precision post-stall landing. Fixed-wing UAVs typically require long, flat runways. The objective of this effort was to land precisely with minimum speed and in minimum distance. Researchers used as little thrust as possible to land from a significant cruise speed and altitude (above the tree line). They explored both a trajectory library approach as well the aforementioned NMPC approach. Both algorithms leveraged radial basis function neural networks to more precisely model the lift, drag, and moment coefficients associated with the post-stall regime. The algorithm was tested on a 60-inch-wingspan, 4.5-kg fixed-wing UAV and demonstrated precision landing in variable wind.

Related Publications

  • Max Basescu, Bryanna Yeh, Luca Scheuer, Kevin Wolfe, Joseph Moore, “Precision Post-Stall Landing using NMPC with Learned Aerodynamics,” IEEE Robotics and Automation Letters (RA-L).
  • Max Basescu, Bryanna Yeh, Luca Scheuer, Kevin Wolfe, Joseph Moore, “Precision Post-Stall Landing using NMPC with Learned Aerodynamics,” 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023).
High-Speed Navigation Using Onboard Vision

Recent advances in electro-optical cameras afford a unique opportunity for lightweight high-frequency sensing of the environment. However, as the speed of robot systems increases, the uncertainty associated with these sensor systems is more likely to negatively impact system performance. Noisy pose estimates from visual odometry, motion blur, and lighting conditions can all contribute significantly to uncertainty. Furthermore, limited field of view (FOV) can lead to overly conservative navigation speeds.

To overcome these challenges, ISC researchers explored integrating their nonlinear model predictive control (NMPC) approaches with computationally lightweight mapping frameworks. In addition, we have also explored machine-learning algorithms such as generative adversarial networks to predict and reason about the environment beyond the sensor’s FOV for more robust and high-speed navigation. 

Fast Motion Planning Using PAC-NMPC

NMPC is typically restricted to short, finite horizons to limit the computational burden of online optimization. This makes a global planner necessary to avoid local minima when using NMPC for navigation in complex environments. For this reason, the performance of NMPC approaches is often limited by that of the global planner. While control policies trained with reinforcement learning (RL) can theoretically learn to avoid such local minima, they are usually unable to guarantee enforcement of general state constraints. In this paper, we augment a sampling-based stochastic NMPC (SNMPC) approach with an RL-trained perception-informed value function. This allows the system to avoid observable local minima in the environment by reasoning about perception information beyond the finite planning horizon. By using Probably Approximately Correct NMPC (PAC-NMPC) as our base controller, we are also able to generate statistical guarantees of performance and safety. The approach was demonstrated in simulation and on hardware using a 1/10th-scale rally car with lidar.

Related Publications

  • Adam Polevoy, Marin Kobilarov, Joseph Moore, “Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC),” IEEE Robotics and Automation Letters (RA-L), Vol. 8, Issue 11 (Nov 2023).

High-Speed Navigation Using Predictive Mapping

Safe high-speed navigation is a key enabling capability for real-world deployment of robotic systems.  A significant limitation of existing approaches is the computational bottleneck associated with explicit mapping and the limited FOV of existing sensor technologies. Researchers in the ISC study algorithmic approaches that allow robots to predict spaces extending beyond the sensor horizon, opening the possibility of robust planning at high speeds and more efficient map exploration.

They accomplish this by using a generative neural network trained from real-world data. They extend their existing control algorithms to support leveraging the predicted spaces to improve collision-free planning and navigation at high speeds. Using a physical robot based on the MIT RACECAR equipped with an RGBD sensor, a team from the ISC was able to show improved navigation performance at high speeds, as well as increased efficiency in exploring and mapping new areas.

Related Publications

  • Katyal, Kapil D., et al., “Uncertainty-Aware Occupancy Map Prediction Using Generative Networks for Robot Navigation,” 2019 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2019).
  • Katyal, Kapil D., et al., “High-speed robot navigation using predicted occupancy maps,” 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2021).

Vision-Based Navigation for Fixed-Wing UAVs

ISC researchers integrated the NanoMap framework with their NMPC approach to enable vision-based collision-free navigation with fixed-wing UAVs. As a result, they reached cruise speeds of 20 mph and demonstrated the ability to execute aggressive maneuvers to avoid obstacles. This approach allows for reasoning about sensing uncertainty, both in the state estimates as well as in the depth measurements used for navigation.

Related Publications

  • Polevoy, Adam, et al., “Post-Stall Navigation with Fixed-Wing UAVs Using Onboard Vision.” 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022).

Complex Terrain Navigation

Complex Terrain Navigation Via Model Error Prediction

Off-road environments contain a wide range of terrains that pose challenges for conventional autonomous navigation systems. In particular, deformable obstacles, such as grasses, bushes, and other foliage, can be difficult to navigate because these objects, although traversable, appear untraversable when viewed with depth sensors.

To address this challenge, a team from the ISC developed a neural network architecture that uses camera imagery to learn a traversability metric. They collect training data by operating an uncrewed ground vehicle in an environment and evaluating the trajectory deviation error. Objects that are traversable generate a low amount of error, while untraversable objects produce large errors. Given a sampled vehicle trajectory and a history of images, the neural network can learn to predict model error. Using this predictive network, researchers are able to traverse long distances through grasslands, shrubs, and bushes and demonstrate robustness to environments outside the training distribution.

Related Publications

  • Polevoy, Adam, et al., “Complex Terrain Navigation via Model Error Prediction.” 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022).
  • Knuth, Craig, Glen Chou, Jamie Reese, Joseph Moore, “Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems,” 2023 IEEE International Conference on Robotics and Automation (ICRA).