Rapid Quadrotor Navigation in Diverse Environments using an Onboard Depth Camera

TL;DR

A reactive planner using forward-arc motion primitives, which leverages a history of RGB-D observations to safely maneuver in close proximity to obstacles is developed, which demonstrates a 24 % increase in success rate compared to state-of-the-art approaches.

Abstract

Search and rescue environments exhibit challenging 3D geometry (e.g., confined spaces, rubble, and breakdown), which necessitates agile and maneuverable aerial robotic systems. Because these systems are size, weight, and power (SWaP) constrained, rapid navigation is essential for maximizing environment coverage. Onboard autonomy must be robust to prevent collisions, which may endanger rescuers and victims. Prior works have developed high-speed navigation solutions for autonomous aerial systems, but few have considered safety for search and rescue applications. These works have also not demonstrated their approaches in diverse environments. We bridge this gap in the state of the art by developing a reactive planner using forward-arc motion primitives, which leverages a history of RGB-D observations to safely maneuver in close proximity to obstacles. At every planning round, a safe stopping action is scheduled, which is executed if no feasible motion plan is found at the next planning round. The approach is evaluated in thousands of simulations and deployed in diverse environments, including caves and forests. The results demonstrate a 24% increase in success rate compared to state-of-the-art approaches.

Figures (12)

• Figure 1: (\ref{['sfig:cave']}) Tight spaces, (\ref{['sfig:industry']}) high amounts of particulate matter such as dust and (\ref{['sfig:forest-glory']}) thin obstacles such as branches are hazards found in search and rescue environments. To aid search teams, autonomous aerial systems must rapidly navigate these dangers without posing additional risks for rescuers or victims. This paper proposes a rapid quadrotor navigation system, which uses forward-arc motion primitives and a forward-facing depth camera to achieve speeds up to $6m\per s$ in cluttered environments. Safety is achieved by executing a safe stopping action when no feasible action is found. Experiments are conducted in diverse environments, including caves and forests. A video of these experiments may be found at https://youtu.be/tk8vUot0gD4
• Figure 2: System diagram of the navigation algorithm. Given depth images and odometry, NanoMap florenceIntegratedPerceptionControl2020 is used for collision avoidance and a library of forward-arc motion primitives is generated for motion planning. To maintain safety, collision-free trajectories are scheduled such that a feasible stopping action is always available within the known free space.
• Figure 3: Derivatives of the scheduled trajectory are continuous up to snap and smooth up to jerk. The planning strategy ensures the robot stops in a safe region.
• Figure 4: Illustrative example of trajectory scheduling with the motion primitive library. The motion primitives in the library are shown in gray. The cost of each primitive is evaluated by computing the Euclidean distance between the endpoint and goal (shown as a pink triangle). The endpoints are shown as dots colored from purple to blue, where more pink indicates closer to the goal. Motion primitives that are in collision are pruned. The primitive with lowest cost (shown in dark gray) is selected for execution. The selected primitive segment is scheduled from times $[t_p, 2t_p)$ and the stopping primitive is scheduled from $[2t_p, 2t_p+T)$.
• Figure 5: (\ref{['sfig:cave1']})--(\ref{['sfig:sewer5']}) illustrate a subset of the simulation environments used to validate the proposed approach.