Autonomous Navigation for Micro-Air Vehicles Using Reciprocal Velocity Obstacles
Hannah R. Kerner, Niti J. Madhugiri, Dinesh Manocha
University of North Carolina at Chapel Hill, Department of Computer Science
Introduction
• Micro Aerial Vehicles (MAVs) and their characteristics:
• small size, ease of accessibility at low altitudes and higher degrees of freedom
• MAVs have enjoyed popularity recently for a variety of applications:
• military attack and defense, security, search & rescue, science experiments, etc.
• Still many engineering challenges and research issues to be resolved for autonomous
navigation of MAVs to be safely integrated into the national airspace
• need to consider the kineodynamic constraints, the load constraints,
environmental factors, limitations on flight duration, safety zone requirements
• navigation issues at low altitudes and random trajectories
• We start by addressing local collision avoidance amongst multiple MAVs. In this work we try
to implement autonomous navigation and collision avoidance using 3D RVO model on real
physical quadcopters.
Kinetic and Dynamic Constraints
RVO2 3D
Physical Agents: Quadcopters
• Velocity obstacles (VO) denote the set of velocities leading to an inevitable collision
• For moving obstacles the VO translates according to the obstacle velocity
• Assembled two quad-rotor helicopters (quadcopters) using the 3D Robotics
ArduCopter Quad DIY Kit
• ArduCopter components include:
Quad Frame: four arms + four rotors; load capacity of 400 grams
ArduPilot Mega 2.6: open-source, multiplatform autopilot; a small hardware
package that includes vehicle-specific firmware
uBlox GPS Module: board plus digital compass; accuracy of 2-3 meters
• In order to avoid collision, choose a velocity outside the VO region.
• However when we have responsive agents, each tries to avoid collision, giving rise to the
concept of Reciprocal Velocity Obstacles (RVO)
Radio Telemetry Kit: interchangeable air and ground modules for wireless
communication between ground-station and ArduCopter; frequency of 915 MHz
Quad
Frame
ArduPilot Mega
2.6
Radio Telemetry
Kit
GPS Module with
Compass
• RVO assumes accurate prediction of other agents future velocities.
• However this assumption can fail, leading us to Optimal Reciprocal Collision Avoidance
(ORCA)
• ORCA identifies a collision given relative velocity and position; finds alternative collision
free velocities; and choses the one that requires minimum change to the current velocity
Current Status and Results
• Simple car model: 2D motion with variable speed and a limit to the maximum steering angle
• Simple airplane model: Extend the simple car model to 3D to vary altitude, but without
reverse gear
• 4D configuration space: {x,y,z,θ}
• Constraints on the maximum speed, maximum altitude, and yaw (steering) angle
• Hence, constraints on accelerations
• This change in velocity is then equally shared among the two agents, enforced by a half
plane constraint.
• Multiple neighbors form multiple simultaneous constraints. And the nearest feasible velocity
is chosen
• This approach is extended to 3D in our consideration
Integration and Interfacing
• Integrated the Ogre3D, an open-source 3D graphics engine, into the RVO2-3D
simulation to visualize results of path computation
• Created a simulation of two quadcopters at positions on the surface of sphere crossing over
to the antipodal position. Real physical dimensions and safety zone considerations were
incorporated into the test case.
• Computed collision free trajectories using RVO 3D, which outputs the x,y,z position of each
quadcopter at each time step.
• Created a visualization of this scenario using Ogre 3D framework integration with RVO,
which displays agent separation for assurance of collision-free paths
• Assembled quadcopters with necessary accessories, firmware, and initial setup/calibration.
Further testing is required before they can be safely used and integrated.
• We are in the process of adding additional quadrotor kineodynamics into the framework and
some filtering to account for uncertainties.
• MAVLink, a communications protocol for micro-air vehicles, was used to share
Limitations and Future scope
information in the form of header-only messages between the hardware and ground
station running RVO2-3D.
• Mission Planner used for configuring settings, loading firmware, and initial testing
• Assessing the feasibility of integrating BRVO, a modification to RVO that uses
statistical inference techniques to compute a motion model from noisy data
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Presently 4 DOF ignoring roll and pitch
Environmental factors like drag and real world constraints are yet to be incorporated
Assumption about perfect sensing: need to account for uncertainties and delays
Static Obstacles and global planner need to be added
There is much future scope for effective sensing of the surroundings & alternatives to GPS