Navite: A Neural Network System For Sensory-Based Robot Navigation

A neural network system, NAVITE, for incremental trajectory generation and obstacle avoidance is presented. Unlike other approaches, the system is effective in unstructured environments. Multimodal inforrnation from visual and range data is used for obstacle detection and to eliminate uncertainty in the measurements. Optimal paths are computed without explicitly optimizing cost functions, therefore reducing computational expenses. Simulations of a planar mobile robot (including the dynamic characteristics of the plant) in obstacle-free and object avoidance trajectories are presented. The system can be extended to incorporate global map information into the local decision-making process.Defense Advanced Research Projects Agency (AFOSR 90-0083); Office of Naval Research (N00014-92-J-l309); Consejo Nacional de Ciencia y Tecnología (63l462

Dynamic Guarding of Marine Assets Through Cluster Control of Automated Surface Vessel Fleets

There is often a need to mark or patrol marine areas in order to prevent boat traffic from approaching critical regions, such as the location of a high-value vessel, a dive site, or a fragile marine ecosystem. In this paper, we describe the use of a fleet of robotic kayaks that provides such a function: the fleet circumnavigates the critical area until a threatening boat approaches, at which point the fleet establishes a barrier between the ship and the protected area. Coordinated formation control of the fleet is implemented through the use of the cluster-space control architecture, which is a full-order controller that treats the fleet as a virtual, articulating, kinematic mechanism. An application-specific layer interacts with the cluster-space controller in order for an operator to directly specify and monitor guarding-related parameters, such as the spacing between boats. This system has been experimentally verified in the field with a fleet of robotic kayaks. In this paper, we describe the control architecture used to establish the guarding behavior, review the design of the robotic kayaks, and present experimental data regarding the functionality and performance of the system.Fil: Mahacek, Paul. Santa Clara University; Estados UnidosFil: Kitts, Christopher A.. Santa Clara University; Estados UnidosFil: Mas, Ignacio Agustin. Santa Clara University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Cluster Control of Automated Surface Vessels

This research focuses on the design and control of a fleet of robotic kayaks, and presents experimental data regarding the functionality and performance of the system. One of the key technical challenges in fielding multi-robot systems for real-world applications is the coordination and relative motion control of the individual units. Coordinated formation control of the fleet is implemented through the use of the cluster space control architecture, which is a full-order controller that treats the fleet as a virtual, articulating, kinematic mechanism. The resulting system is capable of autonomous navigation utilizing a centralized controller, currently implemented via a shore-based computer that wirelessly receives ASV data and relays control commands. Using the cluster space control approach, these control commands allow a cluster supervisor to oversee a flexible and mobile formation formed by the ASV cluster. This paper includes an extended appendix which includes MatLab and Simulink code as well as two publications completed in the process of this research