Researchers at the Massachusetts Institute of Technology (MIT) presented a project at the International Symposium on Experimental Robotics involving an autonomous drone fleet system that collaboratively mapped an environment under dense forest canopy.
Designed with search and rescue in mind, the drones used lidar, onboard computation and wireless communication, with no requirement for GPS positioning.
Each drone carries laser-range finders for position estimation, localization and path planning. As it flies, each drone creates its own 3-D map of the terrain. A ground station uses simultaneous localization and mapping (SLAM) technology to combine individual maps from multiple drones into a global 3-D map that can be monitored by operators.
The MIT team tested its concept via simulations of randomly generated forests, and world-tested two drones in a forested area at NASA’s Langley Research Center. In both experiments, each drone mapped a roughly 20-square-meter area in about two to five minutes, while the control system integrated their maps together in real-time.
The drones were programmed to identify multiple trees’ orientations, as recognizing individual trees in impossible for the technology, and individual trees’ orientation very difficult. When the lidar signal returns a cluster of trees, an algorithm calculates the angles and distances between trees to identify the cluster and determine if it has already been identified and mapped, or is a new mini-environment.
The technique also aids in merging maps from the separate drones. When two drones scan the same cluster of trees, the ground station merges the maps by calculating the relative transformation between the drones, and then fusing the individual maps to maintain consistent orientations.
A team from Draper and the Massachusetts Institute of Technology (MIT) has developed advanced vision-aided navigation techniques for UAVs that do not rely on external infrastructure, such as GPS, detailed maps of the environment or motion capture systems.
When a firefighter, first responder or soldier operates a small, lightweight flight vehicle inside a building, in urban canyons, underground or under the forest canopy, the GPS-denied environment presents unique navigation challenges.
In many cases, loss of GPS signals can cause these vehicles to become inoperable and, in the worst case, unstable, potentially putting operators, bystanders and property in danger.
Attempts have been made to close this information gap and give UAVs alternative ways to navigate their environments without GPS. But those attempts have resulted in further information gaps, especially on UAVs whose speeds can outpace the capabilities of their onboard technologies.
For instance, scanning lidar routinely fails to achieve its location-matching with accuracy when the UAV is flying through environments that lack buildings, trees and other orienting structures.
Finding a Solution
DARPA awarded contracts to Draper and two other industry teams to create UAVs that autonomously sense and maneuver through unknown environments without external communications or GPS under the Fast Lightweight Autonomy (FLA) program. (Photo: Draper)
Working together under a contract with the Defense Advanced Research Projects Agency (DARPA), Draper and MIT created a UAV that can autonomously sense and maneuver through unknown environments without external communications or GPS under the Fast Lightweight Autonomy (FLA) program.
The team developed and implemented unique sensor and algorithm configurations, and has conducted time-trials and performance evaluations in indoor and outdoor venues.
“The biggest challenge with unmanned aerial vehicles is balancing power, flight time and capability due to the weight of the technology required to power the UAVs,” said Robert Truax, senior member of technical staff at Draper. “What makes the Draper and MIT team’s approach so valuable is finding the sweet spot of a small size, weight and power for an air vehicle with limited onboard computing power to perform a complex mission completely autonomously.”
Draper and MIT’s sensor- and camera-loaded UAV was tested in a number of environments ranging between cluttered warehouses and mixed open and tree filled outdoor environments with speeds up to 10 m/s in cluttered areas and 20 m/s in open areas.
The UAV’s missions were composed of many challenging elements, including tree dodging followed by building entry and exit and long traverses to find a building entry point, all while maintaining precise position estimates.
“A faster, more agile and autonomous UAV means that you’re able to quickly navigate a labyrinth of rooms, stairways and corridors or other obstacle-filled environments without a remote pilot,” said Ted Steiner, senior member of Draper’s technical staff. “Our sensing and algorithm configurations and unique monocular camera with IMU-centric navigation gives the vehicle agile maneuvering and improved reliability and safety — the capabilities most in demand by first responders, commercial users, military personnel and anyone designing and building UAVs.”
Draper’s contribution to the DARPA FLA program — documented in a recent research paper for the 2017 IEE Aerospace Conference — was a novel approach to state estimation (the vehicle’s position, orientation and velocity) called SAMWISE — Smoothing And Mapping With Inertial State Estimation.
SAMWISE is a fused vision and inertial navigation system that combines the advantages of both sensing approaches and accumulates error more slowly over time than either technique on its own, producing a full position, attitude and velocity state estimate throughout the vehicle trajectory.
The result is a navigation solution that enables a UAV to retain all six degrees of freedom and allows it to fly autonomously without the use of GPS or any communication with vehicle speeds of up to 45 miles per hour.
The team’s focus on the FLA program has been on UAVs, but advances made through the program could potentially be applied to ground, marine and underwater systems, which could be especially useful in GPS-degraded or denied environments.
