Dynamic Sensor-based Path Planning and Hostile Target Detection with Mobile Ground Robots. Matt Epperson Dr. Timothy Chung

Transcription

1 Dynamic Sensor-based Path Planning and Hostile Target Detection with Mobile Ground Robots Matt Epperson Dr. Timothy Chung

2 Brief Bio Matt Epperson Cal Poly, San Luis Obispo Sophmore Computer Engineer NREIP Intern (Naval Research Enterprise Internship Program)

3 3 Introduction Ground Platoon Project: Create a ground robot platoon that could work together intelligently to provide force protection to high value targets while navigating through unknown, dangerous environments. Hostile Robots

4 4 Introduction Summer Goals: Develop a mobile ground vehicle capable of autonomous navigation through an unknown environment. In addition to navigation, create an application able to detect and provide information of moving obstacles that could constitute a threat to the vehicle.

5 5 Background Info System Hardware Pioneer 3-AT Robot Hokuyo Laser Scanner ArduPilot 2.6 Laptop Software System: Robotic Operating System (ROS)

6 6 Path Planning The Path Planning Problem Path Planning and Navigation are common challenges in autonomous systems. There have been many algorithms developed to deal with this problem such as Potential Fields and Bug algorithms. Rapidly Exploring Random Trees (RRT) is one of the newer methods for path planning that is well suited to nonholonomic vehicles in environments that include obstacles

7 7 Path Planning Rapidly Exploring Random Tree Build Steps 1. Initialize the first node 2. Select a random node from the sample space 3. Find a node nearest to the random node 4. Create a New node towards the Random node 5. Add the new node to the tree λ lhttp://joonlecture.blogspot.com/

8 8 Path Planning Initial Results Zoomed View

9 9 Path Planning Dynamic Path Problem The environment is unknown to the RRT. Information about the environment is only acquired through movement of the robot which allows the sensors to 'see' more. Solution Use a laser range finder to implement a collision checker that discards any node inside an obstacle. In addition the path is incrementally added onto as more environment information is acquired.

10 10 Path Planning State Estimation State knowledge is important to the RRT because each node inside the RRT represents a possible state that the robot could have. In order to get a good state estimate from the odometry/imu/gps an Extended Kalman Filter (EKF), courtesy of the ROS community, is used to fuse the sensor data together to provide a better state estimate. λ

11 11 Path Planning Final Results λ Green: Path created by RRT λwhite: Obstacles seen by LIDAR λ

12 12 Path Planning

13 13 Hostile Detection Motivation While RRT takes care of stationary obstacles it is unsuited to the avoidance of fast moving objects which could constitute a 'threat' to the robot. Solution Run a parallel application that uses the same laser scanner data to check for any non-static obstacle. Application returns data about that threat such as angle of attack and velocity.

14 14 Hostile Detection How It Works The threat detector application compares a previous set of laser data to a current set and looks for places that have considerable change. Since sensors are inherently noisy a filter was used to remove any false positives from the detection algorithm. Center of obstacle is established and compared to previous values as it moves to calculate angle of attack and XY velocities.

15 15 Future Goals Next Summer (Hopefully) Extend robot system for use on multiple ground robots that can work cooperatively. Move threat detection system from a stationary view to dynamic view. Use threat detection program to provide force protection to high value targets. Create a base station to facilitate communication and decision making between multiple ground robots.

16 16 Future Goals NREIP Experience I also got the chance to... - Be part of flight tests for fixed wing planes with the ARSENL lab at Camp Roberts. - Begin to design a potential field system for quad copters using VICON motion capture system.

17 17 Big Thanks NREIP CRUSER Dr. Chung