Multiagent learning with cooperative coevolutionary algorithms is a critical area of research, and is relevant to many real-world applications including air traffic control, distributed sensor network control, and game-theoretic applications such as border patrol. A key difficulty in multiagent learning is the credit assignment problem, where the impact of each...
This paper addresses the adequacy and perspectives of multi-agent system (MAS) models in the context of policy sup-port for agricultural policy makers. The paper starts illustrating what MAS are and how they may be used. The general presenta-tion is followed by the description of an exemplary spatial dynamic model of...
Current research into path planning for Unmanned Aerial Vehicles (UAVs) has placed a major focus on accomplishing the goals of various agents while avoiding collisions between each other. However, there has been little focus on whether the planned trajectories are fair for each agent.
This work provides an answer to...
Efficient coordination is desired for multi-robot systems in many scenarios. In this research, we first provide a multi-robot system to help human workers during tree fruit harvest. We present an auction-based method to coordinate a team of self-propelled bin carriers to retrieve fruit bins. Second, we propose a more general...
Transportation systems are facing safety and operational challenges with a cost of billions of dollars annually in lost production time and wasted fuel. Infrastructure expansion, previously held as a panacea to most transportation challenges has lost its appeal due to financial, land-use and environmental constraints. Interest is surging in intelligent...
There is growing commercial interest in the use of unmanned aerial vehicles (UAVs) in urban environments, specifically for package delivery applications. However, the size, complexity and sheer numbers of expected UAVs makes conventional air traffic management that relies on human air traffic controllers infeasible. To enable UAVs to safely and...
Multiagent approaches are well suited to designing autonomous solutions for systems that feature complex interactions between many individuals such as in autonomous traffic systems and multi-robot exploration systems. However, creating autonomous agents that function effectively in these systems is a challenging task. In these complex environments, agents need informative reward...
The use of autonomous robots in complex exploration tasks is rapidly increasing. Indeed, robots can provide speed and cost effectiveness in many tasks, as well as allow operation in environments that are hostile to humans. In this dissertation we: 1) provide two adaptive navigation algorithms; 2) develop a coordination mechanism;...
Autonomous vehicles (AVs) at varying market penetrations will change traffic flow and highway performance. At AV market penetrations between 0% and 100%, human driven vehicles (HVs) will be interacting with AVs. However, little is known about how HVs interact with AVs. This study attempts to quantify HV headways when following...
Uninhabited aerial vehicles, also called UAVs are currently controller by a combination of a human pilot at a remote location, and autopilot systems similar to those found on commercial aircraft. As UAVs transition from remote piloting to fully autonomous operation, control laws must be developed for the tasks to be...