No abstract

No abstract

No abstract

Real-time optimization of the dynamic flow of vehicle traffic through a network of signalized intersections is an important practical problem. In this paper, we take a decentralized, schedule-driven coordination approach to address the challenge of achieving scalable network-wide optimization. To be locally effective, each intersection is controlled independently by an on-line scheduling agent. At each decision point, an agent constructs a schedule that optimizes movement of the observable traffic through the intersection, and uses this schedule to determine the best control action to take over the current look-ahead horizon. Decentralized coordination mechanisms, limited to interaction among direct neighbors to ensure scalability, are then layered on top of these asynchronously operating scheduling agents to promote overall performance. As a basic protocol, each agent queries for newly planned output flows from its upstream neighbors to obtain an optimistic projection of future demand. This projection may incorporate non-local influence from indirect neighbors depending on horizon length. Two additional mechanisms are then introduced to dampen ``nervousness'' and dynamic instability in the network, by adjusting locally determined schedules to better align with those of neighbors. We present simulation results on two traffic networks of tightly-coupled intersections that demonstrate the ability of our approach to establish traffic flows with lower average vehicle wait times than both a simple isolated control strategy and other contemporary coordinated control strategies that use moving average forecast or traditional offset calculation.

Barlow, Gregory John. Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming. (under the direction of Edward Grant.)Unmanned aerial vehicles (UAVs) have become increasingly popular for many applications, including search and rescue, surveillance, and electronic warfare, but almost all UAVs are controlled remotely by humans. Methods of control must be developed before UAVs can become truly autonomous. While the field of evolutionary robotics (ER) has made strides in using evolutionary computation (EC) to develop controllers for wheeled mobile robots, little attention has been paid to applying EC to UAV control. EC is an attractive method for developing UAV controllers because it allows the human designer to specify the set of high level goals that are to be solved by artificial evolution. In this research, autonomous navigation controllers were developed using multi-objective genetic programming (GP) for fixed wing UAV applications.Four behavioral fitness functions were derived from flight simulations. Multi-objective GP used these fitness functions to evolve controllers that were able to locate an electromagnetic energy source, to navigate the UAV to that source efficiently using on-board sensor measurements, and to circle around the emitter. Controllers were evolved in simulation. To narrow the gap between simulated and real controllers, the simulation environment employed noisy radar signals and a sensor model with realistic inaccuracies. All computations were performed on a 92-processor Beowulf cluster parallel computer. To gauge the success of evolution, baseline fitness values for a successful controller were established by selecting values for a minimally successful controller. Two sets of experiments were performed, the first evolving controllers directly from random initial populations, the second using incremental evolution. In each set of experiments, autonomous navigation controllers were evolved for a variety of radar types.Both the direct evolution and incremental evolution experiments were able to evolve controllers that performed acceptably. However, incremental evolution vastly increased the success rate of Report Documentation PageForm Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB ...

In this paper, we describe a pilot implementation and field test of a recently developed approach to real-time adaptive traffic signal control. The pilot system, called SURTRAC (Scalable Urban Traffic Control), follows the perspective of recent work in multi-agent planning and implements a decentralized, schedule-driven approach to traffic signal control. Under this approach, each intersection independently (and asynchronously) computes a schedule that optimizes the flow of currently approaching traffic through that intersection, and uses this schedule to decide when to switch green phases. The traffic outflows projected by this schedule are then communicated to the intersection's downstream neighbors, to increase visibility of vehicles entering their respective planning horizons. This process is repeated as frequently as once per second in rolling horizon fashion, to provide real-time responsiveness to changing traffic conditions and coordinated signal network behavior. After summarizing this basic approach to adaptive traffic signal control and the domain challenges it is intended to address, we describe the pilot implementation of SURTRAC and its application to a nine-intersection road network in Pittsburgh, Pennsylvania. Both the SURTRAC architecture for interfacing with the detection equipment, hardware controller and communication network at a given intersection and the extensions required to account for unreliable sensor data are discussed. Finally, we present the results of a pilot test of the system, where SURTRAC is seen to achieve major reductions in travel times and vehicle emissions over pre-existing signal timings.

Abstract-Transference of controllers evolved in simulation to real vehicles is an important issue in evolutionary robotics (ER). We have previously evolved autonomous navigation controllers for fixed wing UAV applications using multiobjective genetic programming (GP). Controllers were evolved to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. We successfully tested an evolved UAV controller on a wheeled mobile robot. A passive sonar system on the robot was used in place of the radar sensor, and a speaker emitting a tone was used as the target in place of a radar. Using the evolved navigation controller, the mobile robot moved to the speaker and circled around it. The results from this experiment demonstrate that our evolved controllers are capable of transference to real vehicles. Future research will include testing the best evolved controllers by using them to fly real UAVs.

Evolutionav Robotics (ER) employs population-based artz$cial evolution to develop behavioral robotics controllers. In this paper we focus on the formulorion and application ofafitness selection function /or ER that makes use of intra-population competitive selection. In the cose of behavioral task. such as game playing, intra-population competition can lead to the evolution ofcomplex behaviors. In orderfor this competition to be realized, thefitness of competing controllers must be based mainly on the aggregare success or failure to complete an overall task. However, because initial controller populations are o/ten subminimally competent, and individuals are unable to complete the overall competitive task at RI/, no selective pressure can be generated at the onset of evolution (the Bootstrap Problem). In selection metric. In the final step of the cycle, a genetic algorithm (GA) is applied. The GA uses information generated by the fitness selection function to select and propagate the fittest individuals in the current population to the next generation population.During propagation, controller networks are altered slightly using stochastic genetic operators such as mutation and crossover to produce offspring that make up the next generation of controllers.This cycle is repeated for many generations to train populations of robot controllers to perform a given task. Much of the ER research reported on to date has investieated the evolution of controllers for extremelv~~ ~ ~~~ ~~~ order to accommodate these conflicting elements in selection. we simple-behaviors such as phototaxis [11[21 or objeit formulate a bimodal fitness selection function. This /unction avoidance [31[41. In such cases, fitness selection metrics accommodates sub-minimally competent initial populations in can he formulated by trial and error. The resulting early evolution, but allows for binary success/fnilure competitive selection of controllers basic formulations can include terms that describe sub-behaviors level. Large arbitrarily connected neural neh+ork.based robot as well as simple sensor-actuator responses. With difficulty, controllers were evolved to play the competitive team game and with sufticient knowledge of the dynamics of a Capture the Flag. Results show that neural controllers evolved behavior, this can he extended to evolve controllers for under a variety ofrandifions were competirive with a hand-coded somewhat more complex robot tasks. For example, in [5] knowledge-based controller and could win a modest majoriy of the authors describe the evolution of a coordinated movement task involving several robots. Other examples of games in a large tournament. relatively complex behaviors evolved using complex handhave evolved lo perform at