Pratap Tokekar

Sensor planning for a symbiotic UAV and UGV system for precision agriculture

We study the problem of coordinating an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) for a precision agriculture application. In this application, the ground and aerial measurements are used for estimating nitrogen (N) levels on-demand across a farm. Our goal is to estimate the N map over a field and classify each point based on N deficiency levels. These estimates in turn guide fertilizer application. Applying the right amount of fertilizer at the right time can drastically reduce fertilizer usage. Towards building such a system, this paper makes the following contributions: First, we present a method to identify points whose probability of being misclassified is above a threshold. Second, we study the problem of maximizing the number of such points visited by an UAV subject to its energy budget. The novelty of our formulation is the capability of the UGV to mule the UAV to deployment points. This allows the system to conserve the short battery life of a typical UAV. Third, we introduce a new path planning problem in which the UGV must take a measurement within a disk centered at each point visited by the UAV. The goal is to minimize the total time spent in traveling and measuring. For both problems, we present constant-factor approximation algorithms. Finally, we demonstrate the utility of our system with simulations which use manually collected soil measurements from the field.

A robotic system for monitoring carp in Minnesota lakes

Robotic sensor networks (RSNs) find increasing use in environmental monitoring as they can collect data from obscure, hard-to-reach places over long periods of time. This work reports progress in building a network of small, lightweight robotic rafts that will be used to monitor common carp tagged with radio transmitters across Minnesota lakes. We describe the design and architecture of the robotic raft and demonstrate the robustness of our waypoint navigation algorithm through field tests conducted in various lakes. We also present results from experiments aimed at localizing tagged fish.

Energy-Efficient Path Planning for Solar-Powered Mobile Robots ∗

We explore the problem of energy-efficient, time-constrained path planning of a solar powered robot embedded in a terrestrial environment. Because of the effects of changing weather conditions, as well as sensing concerns in complex environments, a new method for solar power prediction is desired. We present a method that uses Gaussian Process regression to build a solar map in a data-driven fashion. With this map, we perform energy-optimal path planning using a dynamic programming algorithm. We validate our map construction and path planning algorithms with outdoor experiments, and perform simulations on our solar maps to determine under which conditions the weight of added solar panels is worthwhile for a mobile robot.

Devices, systems, and methods for automated monitoring enabling precision agriculture

Addressing the challenges of feeding the burgeoning world population with limited resources requires innovation in sustainable, efficient farming. The practice of precision agriculture offers many benefits towards addressing these challenges, such as improved yield and efficient use of such resources as water, fertilizer and pesticides. We describe the design and development of a light-weight, multi-spectral 3D imaging device that can be used for automated monitoring in precision agriculture. The sensor suite consists of a laser range scanner, multi-spectral cameras, a thermal imaging camera, and navigational sensors. We present techniques to extract four key data products - plant morphology, canopy volume, leaf area index, and fruit counts - using the sensor suite. We demonstrate its use with two systems: multi-rotor micro aerial vehicles and on a human-carried, shoulder-mounted harness. We show results of field experiments conducted in collaboration with growers and agronomists in vineyards, apple orchards and orange groves.

Energy-optimal trajectory planning for car-like robots

When a battery-powered robot needs to operate for a long period of time, optimizing its energy consumption becomes critical. Driving motors are a major source of power consumption for mobile robots. In this paper, we study the problem of finding optimal paths and velocity profiles for car-like robots so as to minimize the energy consumed during motion. We start with an established model for energy consumption of DC motors. We first study the problem of finding the energy optimal velocity profiles, given a path for the robot. We present closed form solutions for the unconstrained case and for the case where there is a bound on maximum velocity. We then study a general problem of finding an energy optimal path along with a velocity profile, given a starting and goal position and orientation for the robot. Along the path, the instantaneous velocity of the robot may be bounded as a function of its turning radius, which in turn affects the energy consumption. Unlike minimum length paths, minimum energy paths may contain circular segments of varying radii. We show how to efficiently construct a graph which generalizes Dubins’ paths by including segments with arbitrary radii. Our algorithm uses the closed-form solution for the optimal velocity profiles as a subroutine to find the minimum energy trajectories, up to a fine discretization. We investigate the structure of energy-optimal paths and highlight instances where these paths deviate from the minimum length Dubins’ curves. In addition, we present a calibration method to find energy model parameters. Finally, we present results from experiments conducted on a custom-built robot for following optimal velocity profiles.

Tracking Aquatic Invaders: Autonomous Robots for Monitoring Invasive Fish

Carp is a highly invasive bottom-feeding fish that pollutes and dominates lakes by releasing harmful nutrients. Recently, biologists started studying the behavior of carp by tagging the fish with radio emitters. The biologists search for and localize the radio-tagged fish manually using a global positioning system (GPS) and a directional antenna. We are developing a novel robotic sensor system in which human effort is replaced by autonomous robots capable of finding and tracking tagged carp. In this article, we report the current state of our system. We present a new coverage algorithm for finding tagged fish and active localization algorithms for precisely localizing them. In addition to theoretical analysis and simulation results, we report results from field experiments.

Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture

We study two new informative path planning problems that are motivated by the use of aerial and ground robots in precision agriculture. The first problem, termed sampling traveling salesperson problem with neighborhoods ( Sampling TSPN), is motivated by scenarios in which unmanned ground vehicles (UGVs) are used to obtain time-consuming soil measurements. The input in SamplingTSPN is a set of possibly overlapping disks. The objective is to choose a sampling location in each disk and a tour to visit the set of sampling locations so as to minimize the sum of the travel and measurement times. The second problem concerns obtaining the maximum number of aerial measurements using an unmanned aerial vehicle (UAV) with limited energy. We study the scenario in which the two types of robots form a symbiotic system—the UAV lands on the UGV, and the UGV transports the UAV between deployment locations. This paper makes the following contributions. First, we present an

Energy-optimal velocity profiles for car-like robots

\operatornamewithlimits{\mathcal {O}}(\frac{r_{\max }}{r_{\min }})

A Robotic Sensor Network for monitoring carp in Minnesota lakes

approximation algorithm for SamplingTSPN , where

Multi-target visual tracking with aerial robots

r_{\min }