The CoBot robots are indoor mobile service robots that autonomously perform tasks in the Gates-Hillman Center at Carnegie Mellon University. While their autonomy is very reliable under normal circumstances, my work focuses on making the CoBots more robust when abnormal situations arise. Examples of these abnormal situations include malfunctioning sensors or actuators, unsafe input or actions from humans, and uncommon events in the building. Two safety problems arise: can robots detect and describe the circumstances of these anomalies? and once these circumstances have been found, can robots make decisions that are robust to these anomalies in the presence of uncertainty?
The CMDragons are a team of agile, omnidirectional robots that use a centralized vision and computing system to perceive and determine how to act upon their world. Since the perception and communication problems are close to solved in this domain, the focus of the team has shifted to effective and efficient coordinated decision-making in a highly dynamic adversarial environment.
In the 2013 tournament, my first year of participation, our team focused on forward-prediction passing, planning by coercing the opponent into disadvantageous configurations, and intelligent marking assignment and placement on defense. These additions, along with the strong base code on which they were built, allowed our team to reach the finals of the 2013 RoboCup world robot soccer tournament. The following video shows 3 of the 30 goals our team scored in the tournament (while allowing only 3 in total):
As autonomous robots move into unsupervised, real world scenarios, they become exposed to adversarial entities or environments that may compromise the integrity of the transmitted sensor data. However robots often have multiple sensors that provide the same information (e.g., a robot's position can be obtained from wheel encoders and GPS data); this redundancy in sensors often provides enough information to detect, with a certain degree of confidence, when data coming from some of the sensors has been compromised. Using the LandShark outdoor ground vehicle as a testing platform, I work on using statistical met hods to enable mobile robots to autonomously detect when a subset of their sensors have been compromised.
Effective autonomous navigation is the most basic task that robots need to perform to be autonomous in unpredictable environments. One of my past projects consisted of extending a dynamical-systems based local path planner to allow non-holonomic mobile robots to navigate safely when faced with the particular challenges of indoor environments, such as clutter, narrow spaces and non-convex obstacles. The modified model reduced local minima problems during autonomous reactive navigation and therefore improved effectiveness of navigation. The following video is an example of our navigation work, demonstrated by a simulation; our agents can complete relatively complicated navigation tasks, even though the only information they have at any time is the relative position of their targets and of any obstacles not occluded from their perspectives.