Patrick A. Plonski

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.

Maintaining connectivity in environments with obstacles

Robotic routers (mobile robots with wireless communication capabilities) can create an adaptive wireless network and provide communication services for mobile users on-demand. Robotic routers are especially appealing for applications in which there is a single mobile user whose connectivity to a base station must be maintained in an environment that is large compared to the wireless range. In this paper, we study the problem of computing motion strategies for robotic routers in such scenarios, as well as the minimum number of robotic routers necessary to enact our motion strategies. Assuming that the routers are as fast as the user, we present an optimal solution for cases where the environment is a simply-connected polygon, a constant factor approximation for cases where the environment has a single obstacle, and an O(h) approximation for cases where the environment has h circular obstacles. The O(h) approximation also holds for cases where the environment has h arbitrary polygonal obstacles, provided they satisfy certain geometric constraints - e.g. when the set of their minimum bounding circles is disjoint.

Environment and Solar Map Construction for Solar-Powered Mobile Systems

Energy harvesting using solar panels can significantly increase the operational life of mobile robots. If a map of expected solar power is available, energy efficient paths can be computed. However, estimating this map is a challenging task, especially in complex environments. In this paper, we show how the problem of estimating solar power can be decomposed into the steps of magnitude estimation and solar classification. Then, we provide two methods to classify a position as sunny or shaded: a simple data-driven Gaussian Process method and a method that estimates the geometry of the environment as a latent variable. Both of these methods are practical when the training measurements are sparse, such as with a simple robot that can only measure solar power at its own position. We demonstrate our methods on simulated randomly generated environments. We also justify our methods with measured solar data by comparing the constructed height maps with satellite images of the test environments, and in a cross-validation step where we examine the accuracy of predicted shadows and solar current.

Finding and tracking targets in the wild: Algorithms and field deployments

We describe our efforts on building a robotic system for detecting and tracking radio-tagged invasive fish using teams of autonomous ground and surface vehicles. In addition to system building and field experiments, our efforts clustered around three fundamental problems: (1) Search: how to find the target as quickly as possible, (2) Active localization: how to actively choose measurement locations to accurately estimate target locations, and (3) Long-term autonomy through energy-efficiency and harvesting. We present specific problem formulations and a summary of our results so far. We conclude the paper with a discussion on our progress and next steps.

Navigation around an unknown obstacle for autonomous surface vehicles using a forward-facing sonar

A robotic boat is moving between two points when it encounters an obstacle of unknown size. The boat must find a short path around the obstacle to resume its original course. How should the boat move when it can only sense the proximity of the obstacle, and does not have prior information about the obstacles size? We study this problem for a robotic boat with a forward-facing sonar. We study two versions of the problem. First, we solve a simplified case when the obstacle is a rectangle of known orientation but unknown dimensions. Second, we study a more general case where an arbitrarily shaped obstacle is contained between two known parallel lines. We study the performance of the algorithms analytically using competitive analysis and present results from field experiments. The experimental setup is relevant for harbor patrol or autonomous navigation in shallow water.

A competitive online algorithm for exploring a solar map

In this paper, we study the problem of quickly building the 3D model of an outdoor environment from measurements obtained by a robot equipped with a solar panel. The robot knows the angle of the sun and the locations of the objects in the environment. It does not know, however, the height of the objects. For example, it might be possible to use satellite images to obtain locations of trees in a field but not their heights. In order to compute the height of an object, the robot must find the projection of the objects highest point. This is where the shadow of the object ends. The robot can find it by tracing the shadow (moving parallel to the sun) until the measurement switches from shadow to sun or vice versa. The robots goal is to compute the height of every object as quickly as possible using only solar measurements. We formulate this as an online optimization problem. The optimal offline algorithm is given by the Traveling Salesman path of the transition points. The robot does not know these locations a priori. It must search for each of them. We present an algorithm with the property that for n objects, our distance traveled is guaranteed to be within a factor O(log n) of this optimal offline tour. In addition to analytical proofs, we demonstrate the algorithm with simulations using solar data collected from field experiments, and examine its performance for uniformly distributed sites.

Approximation algorithms for tours of height-varying view cones

We introduce a novel coverage problem that arises in aerial surveying applications. The goal is to compute a shortest path that visits a given set of cones. The apex of each cone is restricted to lie on the ground plane. The common angle α of the cones represent the field of view of the onboard camera. The cone heights, which can be varying, correspond with the desired observation quality (e.g. resolution). This problem is a novel variant of the traveling salesman problem with neighborhoods (TSPN). We name it Cone-TSPN. Our main contribution is a polynomial time approximation algorithm for Cone-TPSN. We analyze its theoretical performance and show that it returns a solution whose length is at most O ( 1 + log ( h max / h min ) ) times the length of the optimal solution where h max and h min are the heights of the tallest and shortest input cones, respectively.We demonstrate the use of our algorithm in a representative precision agriculture application. We further study its performance in simulation using randomly generated cone sets. Our results indicate that the performance of our algorithm is superior to standard solutions.