In the spring of 2008, Dr. Clark worked with archeologist Dr. Timmy Gambin from Malta to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. They often consisted of several connected chambers, still containing water. A sonar-equipped Remotely Operated Vehicle (ROV) was deployed into the cisterns dating back to 300 B.C. to obtain both video footage and sonar range measurements. Different mapping and localization techniques were employed to construct 2D maps of 6 different cisterns. An additional expedition took place in Spring of 2009, resulting in 26 more sites being mapped. This project was initially funded through a California State Faculty Support Grant, and is now being funded through an NSF IRES grant.

More information about the project can be found at the Malta Cistern Mapping Site.

The ICEX Program is a collaborative effort between California Polytechnic State University’s Computer Engineering Program and various international partners. ICEX is an opportunity for students to apply their technical knowledge in an international context and to increase global citizenship across campus. The 2010 program is the Norway Underwater Robot Sampling Project which has the goal of providing education and new technology to the NORUS Program.

More information about the project can be found at the ICEX Site.

The AUV Shark Tracking project is a new project in which investigators Christopher Lowe (Cal State Long Beach Biological Sciences), Mark Moline (Cal Poly Biological Sciences), and Chris Clark (Cal Poly Computer Science) are collaborating to equip AUVs with the ability to track sharks. Images are courtesy of Christopher Lowe, CSLB.

After obtaining several underwater parameter data sets using AUVs, we are working on novel ways to integrate them into static and dynamic models. Currently we are working on developing 4D maps of dissolved Oxygen, as well as assimilating AUV data into a Regional Ocean Modeling System (ROMS).

The Squid Tracking project is a collaboration between Pat Fidopiastis (Cal Poly Biological Sciences) and Chris Clark (Cal Poly Computer Science). In the summer of 2009, researchers travelled to shallow water beaches of Hawaii to find and track small squid using the VideoRay Pro III ROV.

The NORUS programme ‘Technology development for marine monitoring and ocean observation’ is a novel research-based higher education programme with a scientific focus on how climate-induced changes impact the living resources and ecosystems in the Arctic. The program is funded through a grant from the Norwegian Partnerships with North America Program.

More information about the project can be found at the NORUS Site.

As robots continue advancing and permeating our world, the frequency of interactions between robots that do not share a common goal will increase. Each robot in the world will likely have a different fitness function to optimize due to each robot having independent goals and owners. Such collections of robots, termed a Multi-Robot Community (MRC), generally, have no shared global fitness function to maximize. This is in contrast to most Multi-Robot Systems (MRSs), which are designed to cooperate to accomplish common goals and hence have a shared global fitness function. A MRC may contain individual robots, MRSs, or a combination. To establish cooperative task allocation in MRCs, a new task allocation framework is required.

Using a reward based system to facilitate coordination is impractical in a MRC, where a reward for accomplishing one robots task has no value for another robot. Rather, coordination in a MRC is focussed on reducing robot costs. To accomplish this, robots build relationships with other robots. These relationships, mimicking those in humans, grow over time between willing parties through robots performing altruistic actions for other robots. Robots performing an altruistic action incurs a cost without any reward or promise of payback. The relationships evolve while still protecting against selfishness. Three altruistic controllers have been developed that model the reciprocating altruistic relationships. The first, termed one-to-one, allows relationships to form only between pairs of robots. The second, termed one-to-many, allows relationships to form between individual robots and sets of robots. The third, termed one-to-mixed, is a hybrid of the previous two controllers.

This work concerns the use of a cooperative multiple robot system as applied to distributed tracking and sampling of a boundary edge. Within this system the boundary edge is partitioned into subsegments, each allocated to a particular robot such that workload is balanced across the robots. Also, to minimize the time between sampling local areas of the boundary edge, it is desirable to minimize the difference between each robot’s progression (i.e. phase) along its allocated sub segment of the edge. The paper introduces a new distributed controller that handles both workload and phase balancing. Simulation results are used to illustrate the effectiveness of the controller in an Autonomous Underwater Vehicle (AUV) under ice edge sampling application. Experimentation with three iRobot Creates are also conducted.

Dr. Clark and students have developed several solutions to various multi-robot motion planning problems. Some have been based on Probabilistic Road Maps (PRMs), while others extend A*.

In 2008, Dr. George Bekey, Chris Clark and students enabled an RS Media with the ability to navigate autonomously.

The Waterloo Modular and Reconfigurable Robot (MRR) was designed with William Melek (Waterloo Mechanical Engineering) and students.

This work addresses the problem of allocating closely spaced targets to multiple autonomous underwater vehicles in the presence of constant ocean currents. The main difficultly of this problem is that the nonholonomic vehicles are constrained to move along forward paths with bounded curvatures. The proposed algorithm solves the task allocation problem with market-based auctions that minimize the total travel time to complete the mission. By considering the dynamics and kinematics of the vehicle as well as the effect of ocean currents within the cost function, the proposed algorithm is able to create feasible paths with a lower cost when compared to solutions whose cost functions are calculated based solely on Euclidean distances.