Aaron Gage

Emotion-based control of cooperating heterogeneous mobile robots

Previous experiences show that it is possible for agents such as robots cooperating asynchronously on a sequential task to enter deadlock, where one robot does not fulfil its obligations in a timely manner due to hardware or planning failure, unanticipated delays, etc. Our approach uses a formal multilevel hierarchy of emotions where emotions both modify active behaviors at the sensory-motor level and change the set of active behaviors at the schematic level. The resulting implementation of a team of heterogeneous robots using a hybrid deliberative/reactive architecture produced the desired emergent societal behavior. Data collected at two different public venues illustrate how a dependent agent selects new behaviors (e.g., stop serving, move to intercept the refiner) to compensate for delays from a subordinate agent (e.g., blocked by the audience). The subordinate also modifies the intensity of its active behaviors in response to feedback from the dependent agent. The agents communicate asynchronously through knowledge query and manipulation language via wireless Ethernet.

Application of the Distributed Field Robot Architecture to a Simulated Demining Task

As mobile robot teams become more complex, it is necessary to develop a control architecture to manage the resources present in the team. The Distributed Field Robot Architecture (DFRA) is a distributed, object-oriented implementation of the SFX hybrid robot architecture that allows for dynamic discovery and acquisition of robot resources and the seamless integration of humans and artificial agents in the robot team. This paper introduces the DFRA and details its application to a high-fidelity demining scenario using a heterogeneous team of ground and aerial robots.

Principles and experiences in using legos to teach behavioral robotics

This paper describes the application of lego mindstorms and vision command kits as a cost- and time-effective means of reinforcing behavioral robotics principles to students of different disciplines with limited programming skills. As part of a course in robotics, senior undergraduate and first year graduate students in computer science, engineering, and psychology have worked in small groups building and programming robots to perform a variety of tasks, ultimately developing robots for a mock search and rescue operation. This paper discusses the pedagogical principles, the exercises, student reactions, shortcomings, and lessons learned. The laboratory exercises were used to teach students in two locations (Tampa, Florida and Reykjavik, Iceland) with positive student reviews. The laboratory manual is available to teachers by request, along with the instructors guide to Introduction to AI robotics. Based on our experiences, we recommend their use.

Sensor scheduling in mobile robots using incomplete information via Min-Conflict with Happiness

This paper develops and applies a variant of the Min-Conflict algorithm to the problem of sensor allocation with incomplete information for mobile robots. A categorization of the types of contention over sensing resources is provided, as well as a taxonomy of available information for the sensor scheduling task. The Min-Conflict with Happiness (MCH) heuristic algorithm, which performs sensor scheduling for situations in which no information is known about future assignments, is then described. The primary contribution of this modification to Min-Conflict is that it permits the optimization of sensor certainty over the set of all active behaviors, thereby producing the best sensing state for the robot at any given time. Data are taken from simulation experiments and runs from a pair of Nomad200 robots using the SFX hybrid deliberative/reactive architecture. Results from these experiments demonstrate that MCH is able to satisfy more sensor assignments (up to 142%) and maintain a higher overall utility of sensing than greedy or random assignments (a 7-24% increase), even in the presence of sensor failures. In addition, MCH supports behavioral sensor fusion allocations. The practical advantages of MCH include fast, dynamic repair of broken schedules allowing it to be used on computationally constrained systems, compatibility with the dominant hybrid robot architectural style, and least-disturbance of prior assignments minimizing interruptions to reactive behaviors.

Driving evaluation of persons with disabilities using an advanced vehicle interface system

This paper presents a new approach to the driving assessment of persons with disabilities in which an advanced vehicle interface system is introduced. This system combines a six-degree-of-freedom force reflecting haptic device and a commercially available vehicle modification system. Innovative ergonomic tasks are presented to determine the appropriate position and orientation of the required driving input device. Further, model based computer assistance is incorporated by using assistance functions such as scaling and tremor filtering. Results, obtained from testing a subject with Muscular Dystrophy (MD), demonstrated that the quantitative ergonomic measurements obtained successfully determined the appropriate position and orientation of the modified steering wheel device.

Sensor allocation for behavioral sensor fusion using min-conflict with happiness

For mobile robots employing reactive behaviors, allocation of physical sensors to satisfy sensing needs should be dynamic and fast. It is becoming increasingly apparent that this allocation should also support behavioral sensor fusion, as indicated by experimental data, in order to maximize the use of available sensing hardware and to increase the quality of sensing. These issues are addressed in the context of the min-conflict with happiness algorithm for dynamic sensor allocation, whose execution rates on two real robots ranged from 11 to 17 milliseconds. Experimental results are shown which illustrate the improvements (27.5%-75% of observations) achieved using sensor fusion. The paper also contributes a quantitative representation of sensing quality using t-norms, allowing fused sensors to be compared with single sensors for a behavior.