A Hierarchical Structure for Hybrid Control Applied to Multi-Robot Autonomous Systems

1. A Hierarchical Structure for Hybrid Control Applied to Multi-Robot Autonomous Systems Faculty: Dr. V. Kumar, Dr. J. P. Ostrowski, Dr. C. J. Taylor, Dr. M. Mintz Students: Kenneth A. McIsaac, Aveek K. Das, Joel M. Esposito, John Spletzer, … Funding: DARPA MARS Project

2. Abstract • We use hybrid system theory to describe systems governed by a set of continuous operation modes and transitions between these modes. • We seek to enable rapid development of test applications and to allow easy performance upgrades. • Our structure is based on the creation of agents to process inputs and outputs at the highest level of abstraction (the application domain). The formalism chosen is based on the CHARON modeling language. • We demonstrate our implementation of this structure on two systems: • a nonholonomic car-like robot • a quadrupedal robotic pet

3. Basic Concepts in Hierarchical, Modal Structure • XLSM- A logical sensor is a software construct that converts raw sensor data into some application domain information. The extended logical sensor (XLS) is an agent, which can switch modes based on state information. The XLS interface is defined by the application, it does not change with changes to measured data or XLS implementation. • XLAM- A logical actuator converts application domain commands into physical actuator outputs. The extended logical actuator(XLA) is an agent capable of performing some low-level “inner loop” control and reporting completion of tasks to the planner. • Planner - The planner module performs the system’s high-level decision-making. Using state information and input from the XLSM, it sends a series of high level commands to the XLAM. Planners are designed exclusively in the application domain. They should be reusable across a broad range of hardware platforms given the same set of XLA primitives and XLS data. Planner Extended Logical Sensor Module (XLSM) Extended Logical Actutator Module (XLAM) Robot hardware

4. XLSM command from Planner report to planner to Planner XLAM XLS Interface to XLAM Controllers Go In Direction from XLSM Target Position Not Moving Range map Track Object Back Out Edge Detection Segment Color Frame Difference Actuator Speed Steer from Image Grabber to radio controller Application #1: Clodbuster • Platform: The Clodbuster is a radio-controlled car-like robot used for our MARS experiments. The primary sensor used is an omnidirectional camera. • Description of XLS capabilities: • building a range map of the environment based on edge detection • segmenting known colors from a color image • frame differencing on successive images to detect motion

5. Application #2: AIBO • Platform: AIBO is a quadruped, dog-like robot developed by Sony, equipped with a variety of sensors including nose-mounted color camera, microphones, and inertial, infra-red and touch sensors. The actuators are servomotors controlling the legs, tail, head, and mouth. • Description of software: • The XLSM has three levels of abstraction as shown. The hierarchical structure allows us to add new XLS with added or modified functionality if the new agent implements the interface defined by the XLS object.

6. Application #2: AIBO • Description of software (continued): • The XLAM has two major levels of abstraction. • The XLA layer for the soccer application are implemented as simple closed loop control laws, using XLS data as sampled quantities. • The implementation of the hardware interface layer can be changed without affecting any higher level code. • We have created several different planner agents which execute using the same XLSM and XLAM interfaces. • The Attacker is the agent used to play the soccer game. An Attacker moves to the ball until it is in its possession, then turn to find the goal while keeping the ball at a constant distance. When the ball and goal are lined up, it marches forward in an effort to score. • The Goalie planner protects the goal by switching between three modes. One mode is to move in between the ball and the goal. In the second mode the Goalie tries to re-orient itself with the goal line and return to its goal-keeping position. Finally, if it loses confidence in self position, it does localization. • The Follower planner is for our MARS application. This has two basic modes. If a teammate (same color) is closer than the ball, the Follower tracks his teammate’s movements at a fixed distance. If the ball is closer, it tracks the ball.

7. Conclusions and Future Work • By separating the control problem into three parts (the XLAM, XLSM, and planner) and specifying abstract interfaces between these components, we are able to achieve: • better code organization through the adoption of a common hybrid system formalism (CHARON) • faster development times since modifications to one component no longer affect others • the ability to reuse software components (i.e. the same planner can be used on both wheeled and legged robots) • Future work includes: • extending this paradigm to controlling teams of robots • testing re-usable code capabilities and higher level language CHARON • implementing a variety of new sensors (XLS’s) and actuator modes (XLM’s)