This paper describes an implementation of the 3T robot architecture which has been under development for the last eight years. The architecture uses three levels of abstraction and description languages which are compatible between levels. The makeup of the architecture helps to coordinate planful activities with real-time behaviours for dealing with dynamic environments. In recent years, other architectures have been created with similar attributes but two features distinguish the 3T architecture: (1) a variety of useful software tools have been created to help implement this architecture on multiple real robots; and (2) this architecture, or parts of it, have been implemented on a variety of very different robot systems using different processors, operating systems, effectors and sensor suites.

Experiences with an architecture for intelligent, reactive agents

NASA scenarios for lunar and planetary missions include robotic vehicles that function in both teleoperated and semi-autonomous modes. Under teleoperation, on-board stereo cameras may provide 3-D scene information to human operators via stereographic displays; likewise, under semi-autonomy, machine stereo vision may provide 3-D information for obstacle avoidance. In the past, the slow speed of machine stereo vision systems has posed a hurdle to the semi-autonomous scenario; however, recent work at JPL and other laboratories has produced stereo systems with high reliability and near real-time performance for low-resolution image pairs. In particular, JPL has taken a significant step by achieving the first autonomous, cross-country robotic traverses (of up to 100 meters) to use stereo vision, with all computing on-board the vehicle. Here, we describe the stereo vision system, including the underlying statistical model and the details of the implementation. The statistical and algorithmic aspects employ random field models of the disparity map, Bayesian formulations of single-scale matching, and area-based image comparisons. The implementation builds bandpass image pyramids and produces disparity maps from the 60×64 level of the pyramids at rates of up to two seconds per image pair. All vision processing is done in one 68020 augmented with Datacube image processing boards. We argue that the overall approach provides a unifying paradigm for practical, domain-independent stereo ranging. We close with a discussion of practical and theoretical issues involved in evaluating and extending the performance of the stereo system.

Stereo vision for planetary rovers: stochastic modeling to near real-time implementation

The model-based vision system described in this paper allows a mobile robot to navigate indoors at an average speed of 8 to 10 m/min using ordinary laboratory computing hardware of approximately 16 MIPS power. The navigation capabilities of the robot are not impaired by the presence of stationary or moving obstacles. The vision system maintains a model of uncertainty and keeps track of the growth of uncertainty as the robot travels toward the goal position. The estimates of uncertainty are then used to predict bounds on the locations and orientations of landmarks expected to be seen in a monocular image. This greatly reduces the search for establishing correspondence between the features visible in the image and the landmarks. Given a sequence of image features and a sequence of landmarks derived from a geometric model of the environment, a special aspect of our vision system is the sequential reduction in the uncertainty as each image feature is matched successfully with a landmark, allowing subsequent features to be matched more easily; this is a natural by-product of the manner in which we use Kalman filter-based updating.

Fast vision-guided mobile robot navigation using model-based reasoning and prediction of uncertainties

Execution monitoring in robotics: A survey

Most autonomous outdoor navigation systems tested on actual robots have centered on local navigation tasks such as avoiding obstacles or following roads. Global navigation has been limited to simple wandering, path tracking, straight-line goal seeking behaviors, or executing a sequence of scripted local behaviors. These capabilities are insufficient for unstructured and unknown environments, where replanning may be needed to account for new information discovered in every sensor image. To address these problems, the authors developed a complete system that integrates local and global navigation. The local system uses a scanning laser rangefinder to detect and avoid obstacles. The global system uses an incremental path planning algorithm to optimally replan the global path for each detected obstacle. A control arbiter steers the robot to achieve the proper balance between safety and goal acquisition. This system was tested on a real robot and successfully drove it 1.4 kilometers to find a goal given no a priori map of the environment.

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A complete navigation system for goal acquisition in unknown environments

A path planner and an execution monitoring planner that will enable the rover to navigate to its various destinations safely and correctly while detecting and avoiding hazards are described. An overview of the complete architecture is given. Implementation and testbeds are described. The robot can detect unforeseen obstacles and take appropriate action. This includes having the rover back away from the hazard and mark the area as untraversable in the rover's internal map. The experiments have consisted of paths roughly 20 m in length. The architecture works with a large variety of rover configurations with different kinematic constraints. >

M.G. Slack

Papers

Path planning and execution monitoring for a planetary rover