iMagicSenior Project
Emre AYDINAsil Kaan BOZCUOĞLU
Onur ÖZBEKEgemen VARDAROnur YÜRÜTEN
Project Supervisor: Asst. Prof. Uluç Saranlı
Introduction - MAGIC 2010
• Multi Autonomous Ground-robotic International Challenge
• Jointly sponsored by the US and Australian Departments of Defense
• Robots that will conduct a mission of intelligence, surveillance and reconnaissance
• Mission objectives:– Accurately and completely exploring and mapping the
challenge area– Correctly locating, classifying and recognising all simulated
threats
Introduction – MAGIC 2010 (cont.)
• Program goals:– Accelerating the development of autonomous and
unmanned vehicle technology.– Demonstrating that multi-UVS cooperatives can
operate effectively with limited supervision by humans in realistic environments.
– Bringing fresh insights to the problem of developing robust autonomous multi-vehicle cooperatives and to identify and transition technologies to meet emerging requirements.
Introduction - iMagic
• iMagic is an implementation of the contest in a simulated environment.
• Development of the two opposing teams:– Incursion robots– Objects of interest
• Project goals:– Efficient decision-making and planning algorithms– Task allocation and re-allocation for multiple robots– Full autonomy– Reliance on sensory information exclusively
Network Structure
Communication Between Services
Message Handling
Problems We Have Encountered
• A completely new technology (CCR & DSS)• Complications due to asynchrounous and
distributed computing• Bugs related to the physics engine
Odometry
• Robotics Studio does not have encoder in standard configurations
• We implemented our own encoders with following formula:
rotation = update_elapsed_time * rotation_scale(1/2*pi)*wheel_speed
Odometry (cont’d)
• The formulae:
OT+1 = OT + (DR - DL) / W
DT,T+1 = (DR + DL) / 2
XT+1 = XT + DT,T+1cos(OT+1) YT+1 = YT + DT,T+1sin(OT+1)
Odometry (cont’d)
• Odometry can be erroneous• Our case:
- In mapping, we don’t see much error - That can be because we implement the encoders virtually.
Mapping
• One of the main goal of the Magic 2010
• While the target OOIs are neutralized, mapping process also must be done.
Mapping Algorithm (cont’d)
• Occupancy Map Principle- Every pixel has a value. ( Default: 128)- Robots’ laser range finders measure
distance in 180 degree.- If it returns max range, corresponding pixel values in the range are increased.-Otherwise, the value of the end point of the corresponding ray is decreased.
Incursion Team Robots
• Pioneer 3DXEquipments: - Laser Range Finder - Differential Drive on two wheels
- Web cam
Incursion Team Robots (Cont.)
• They can be in either of the following 3 states:– Wander
– OOI Following
– Motion-to-goal
Incursion Team Robots (Cont.)
• ‘Wander’ State:– Default state
– Move randomly and explore the environment
Incursion Team Robots (Cont.)
• ‘OOI Following’ State:– Follow an OOI until neutralizing it
• Neutralizing an OOI:– Keep the OOI under surveillance for 20 seconds.– Neutralized OOIs are removed from the
environment.
Incursion Team Robots (Cont.)
• ‘Motion-to-goal’ State:– Try to reach a goal point
– For fast and deterministic exploration
Incursion Team Robots (Cont.)
• How is collision avoidance handled?– Slowing down
– Doing boundary following
– Backing off
Incursion Team & Command Center
• Switching to OOI Following State:– Command center calculates the distance between
incursion robots and OOIs.– If it finds an ‘available’ incursion robot close
enough to an OOI, it sends a message to that incursion robot.
Incursion Team & Command Center
• Switching to ‘Motion-to-goal’ State:– Command center determines the unexplored
regions in the map built so far.– Generates a random point in the most unexplored
region; finds an available incursion robot, and assigns that point as a goal to that robot.
OOI Robots’ Motion Goal
Provide a collision-free motion Patrol within a subregion
Robot Information
Lego mindstorm NXT Actuators and Sensors
Differential drive on two wheels Laser range finder Webcam
Lego Mindstorm NXT
Motion Planning Algorithm
“Node construction” phase “Patrol” phase Local planner: Artificial potential fields
Node Construction Produce # of samples = # of existing
nodes
Node Construction (cont.) Randomly pick one of the samples
Node Construction (cont.) Connect the sample with up to k (k=3)
neighbours of the previous node
Node Construction (cont.)
Node Construction (cont.)
Node Construction (cont.)
Node Construction (cont.)
Node Construction (cont.)
Patrol Select target from the node set
Patrol Generate additional “bridge” nodes if
needed.
Analysis
Incremental node construction Avoiding massive pre-computation Motion is not delayed
Implicitly building “borders” Tentative area for patrolling
Collision-free, elegant motion
