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Reliable Off Road Autonomous Vehicles in Challenging Environments

Explore the DARPA PerceptOR program by Carnegie Mellon University, analyzing autonomous ground mobile robot technologies for enhanced reliability, autonomy, and speed in challenging terrains. The project includes man-machine team systems, performance metrics evaluation, and advanced motion planning architectures.

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Reliable Off Road Autonomous Vehicles in Challenging Environments

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  1. Reliable Off Road Autonomous Vehicles in Challenging Environments DARPA Program Carnegie Mellon University

  2. DARPA Program • DARPA PerceptOR • “Perception for Off Road Navigation” • Followed by LAGR (Learning Applied to Ground Robots) • Outdoor autonomous mobility research • 3 years • 500,000 lines of source code • 30 man years of effort

  3. PerceptOR • Goal • Project : Assess the readiness of technologies for autonomous ground mobile robots • System: Maximize autonomy, reliability, and speed • System • Man-machine team • One (remote) operator • One ground robot - UGV • One air robot - UAV

  4. PerceptOR • Performance • Metrics that quantify performance • # of times of intervention by field personnel • Emergency stops by robot, operator, field personnel • Communication bandwidth • System ability to reach waypoints • Instantaneous and average speeds • Distance • Time • All data is recorded for further analysis

  5. PerceptOR

  6. PerceptOR • Blind evaluative testing • Unrehearsed testing • Remote Operator • Only video and data telemetry information • Provided GPS waypoints • Field team • Follow robot in field for robot safety (ESTOP) and data collection • Analysis team • Access to all data, “Situation room” • Assess or correlate among what the robot thinks, operator thinks, and what is really happening,

  7. Unmanned Vehicles • Unmanned Aerial Vehicle • - GPS, IMU • Computers • Ladar and stereo sensing • Range and obstacle data • - Ladar • Cameras • Unmanned Ground Vehicle • INU, computers • range image perception, • local/global planning, • video, stereo ranging, • control • Pose Sensors • Mapping, Obstacle Sensors • Teleoperation Sensors

  8. Mutli-Layer Architecture

  9. Reactive Planning and Control • Monitor system health and react to danger • Closed coordinated control loops using information from above layers of multi-layer design (vehicle state estimates from estimator – layer above) • Trajectory following • Timeout Exceptions • Collision Exceptions

  10. Perceptive Autonomy • Processing sensor information • Proximal and/or immediately visible environment

  11. Mapping • Process of recording, organizing, transforming, registering, resampling, and fusing multiple sources of information

  12. Motion Planning • Motion planning architecture • Local region planning • High fidelity models of vehicle shape and maneuverability for reliable obstacle avoidance • Online convolution of rectangular vehicle model for a number of continuous paths, derived from terrain map encoding elevation as a continuous variable • Ranger local planner • Global planning • Less detailed models of both vehicle and environment • Heuristic search in an 8-connected grid where the convolution is approximated and pre-computed into a 2D configuration space • D* global planner • Estimated cost of local portion of each alternative is assed to the estimated cost of the global portions, and a penalty is added for any misalignment of the two portions at a point of joining

  13. Motion Planning • Motion planning architecture

  14. Local Motion Planning • Designed for high speeds over barren terrain • High speed motions - constraint • Forward modeling is used to predict response trajectories from a pre-stored set of steering and speed commands, which are intended to span the space of feasible motions more or less uniformly

  15. Local Motion Planning

  16. Global Motion Planning • Required to estimate the remaining path cost from the ends of the local path planner’s candidate paths to the next goal • D* algorithm • Finds optimal (lowest-cost) path between the current location and the goal • Real time • Switched to Field D* • Uses interpolation so paths not need to pass through vertices of the grid • Interpolation-based smoothing during the planning process to select paths much closer to true optimal

  17. Behaviors • The fusion of the local and global planners are used when the system is moving at reasonalbe speed over the terrain • Navigate behavior • If errors in global map, no map, etc., • vehicle will stop. • Prioritized behaviors are utilized to deal with the stopped vehicle • Navigate – moving with planners • Lookaround – scan area • Goaround – reactive backup and turn maneuver (multiple times) • Planaround – nonholonomic motion planner • If fail, ask for operator assistance

  18. Project Results

  19. Project Results

  20. Project Results

  21. Project Results

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