John Broderick

Optimal coverage trajectories for a UGV with tradeoffs for energy and time

Area coverage is a common task for an unmanned ground vehicle (UGV) that requires time and energy to complete. We have developed a novel cost function that can be used to optimally traverse a path that covers a region. The UGV model and cost function are developed theoretically and verified experimentally. Our cost function weights force inputs, area covered and motor efficiency to create an optimal trajectory. This trajectory is constrained to follow a coverage path described in the literature. The path is modified based on the cost function by replacing turn-in-place maneuvers by moving turns. Tradeoffs are presented for three cases: (1) drive motor efficiency is not considered, (2) the motors are most efficient at the maximum velocity, and (3) the motors are most efficient below the maximum velocity. Optimality tradeoffs include the time required to cover the region, and the energy required to complete the trajectory. Experimental results using an iRobot Packbot are presented.

Energy usage for UGVs executing coverage tasks

Proper management of the available on-board battery power is critical for reliable UGV operations. In this paper, we will focus on the task of area coverage - in which a UGV is required to move through an area and travel within a certain distance of each point - with limited available energy. We compare coverage paths generated by existing methods and generate optimal trajectories by using a novel cost function. Using an iRobot Packbot, we present results showing dierences in energy usage while following these trajectories. We also compare the energy usage of the Packbot while traveling at a dierent velocities. Our results show that it is more ecient, when traveling at a constant velocity, to travel at a faster velocity by computing the ratio of the energy used to distance traveled.

Modeling and simulation of an unmanned ground vehicle power system

Long-duration missions challenge ground robot systems with respect to energy storage and efficient conversion to power on demand. Ground robot systems can contain multiple power sources such as fuel cell, battery and/or ultra-capacitor. This paper presents a hybrid systems framework for collectively modeling the dynamics and switching between these different power components. The hybrid system allows modeling power source on/off switching and different regimes of operation, together with continuous parameters such as state of charge, temperature, and power output. We apply this modeling framework to a fuel cell/battery power system applicable to unmanned ground vehicles such as Packbot or TALON. A simulation comparison of different control strategies is presented. These strategies are compared based on maximizing energy efficiency and meeting thermal constraints.

Supervisory traction control for a slipping UGV

Unmanned Ground Vehicles (UGVs) face many mobility challenges as they operate in unstructured environments. In addition to insurmountable obstacles, UGVs can lose traction and slip, reducing maneuverability and potentially causing collisions with obstacles. This paper develops a dynamic model of a differentially-driven ground robot with lateral and longitudinal slip, using a ground friction model from the literature. A novel switching control system, with velocity input commands, is also presented. The controller switches from PID control to a trajectory planning mode when the robot begins to slip or when the normal command would exceed the ground friction forces. The trajectory is planned and executed based on commanded velocities. Stability and robustness of the trajectory planning controller are discussed. This control law is compared in simulation to a PID controller, using the UGV model, on different surfaces. The traction controller drives the UGV closer to the desired path derived from the reference trajectories.

Maximizing Coverage for Mobile Robots While Conserving Energy

This paper presents a method to compare area coverage paths in the context of energy efficiency. We examine cover-age paths created from the Boustrophedon Decomposition and Spanning Tree methods in an optimal control setting. Our cost function weights the force inputs to drive the robot and the currently uncovered region. We derive an optimal traversal of the path in a point-to-point manner. In particular, we introduce a meas function that represents the percentage of the area that is still to be visited. The effect of meas on the optimal traversal is derived. Trade-offs between area covered versus the time and energy required are presented. A simple trajectory modification allows the vehicle to continue moving through a turn to reduce energy consumption.© 2012 ASME

Anomaly detection without a pre-existing formal model: Application to an industrial manufacturing system

Some faults in manufacturing systems that are evident in event-based data cannot be detected by existing solutions. This paper summarizes a method for identifying anomalies in event-based data using model generation. The method is based on knowledge of events and resources of the system and generates a set of Petri Net models to detect the anomalies. The method is applied to an industrial machining cell that has been experiencing a gantry waiting problem. The anomaly detection solution is able to accurately identify the gantry waiting anomaly and another anomaly that occurred right before the gantry waiting issue, indicating a possible cause.