Brian Coltin

Corrective Gradient Refinement for mobile robot localization

Particle filters for mobile robot localization must balance computational requirements and accuracy of localization. Increasing the number of particles in a particle filter improves accuracy, but also increases the computational requirements. Hence, we investigate a different paradigm to better utilize particles than to increase their numbers. To this end, we introduce the Corrective Gradient Refinement (CGR) algorithm that uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. We develop an observation model for mobile robot localization using point cloud sensors (LIDAR and depth cameras) with vector maps. This observation model is then used to analytically compute the state space gradients necessary for CGR. We show experimentally that the resulting complete localization algorithm is more accurate than the Sampling/Importance Resampling Monte Carlo Localization algorithm, while requiring fewer particles.

Mobile robot task allocation in hybrid wireless sensor networks

Hybrid sensor networks consisting of both in-expensive static wireless sensors and highly capable mobile robots have the potential to monitor large environments at a low cost. To do so, an algorithm is needed to assign tasks to mobile robots which minimizes communication among the static sensors in order to extend the lifetime of the network. We present three algorithms to solve this task allocation problem: a centralized algorithm, an auction-based algorithm, and a novel distributed algorithm utilizing a spanning tree over the static sensors to assign tasks. We compare the assignment quality and communication costs of these algorithms experimentally. Our experiments show that at a small cost in assignment quality, the distributed tree-based algorithm significantly extends the lifetime of the static sensor network.

CoBots: Collaborative robots servicing multi-floor buildings

In this video we briefly illustrate the progress and contributions made with our mobile, indoor, service robots CoBots (Collaborative Robots), since their creation in 2009. Many researchers, present authors included, aim for autonomous mobile robots that robustly perform service tasks for humans in our indoor environments. The efforts towards this goal have been numerous and successful, and we build upon them. However, there are clearly many research challenges remaining until we can experience intelligent mobile robots that are fully functional and capable in our human environments.

Multi-humanoid world modeling in Standard Platform robot soccer

In the RoboCup Standard Platform League (SPL), the robot platform is the same humanoid NAO robot for all the competing teams. The NAO humanoids are fully autonomous with two onboard directional cameras, computation, multi-joint body, and wireless communication among them. One of the main opportunities of having a team of robots is to have robots share information and coordinate. We address the problem of each humanoid building a model of the world in real-time, given a combination of its own limited sensing, known models of actuation, and the communicated information from its teammates. Such multi-humanoid world modeling is challenging due to the biped motion, the limited perception, and the tight coupling between behaviors, sensing, localization, and communication. We describe the real-world opportunities, constraints and limitations imposed by the NAO humanoid robots. We contribute a modeling approach that differentiates among the motion model of different objects, in terms of their dynamics, namely the static landmarks (e.g., goal posts, lines, corners), the passive moving ball, and the controlled moving robots, both teammates and adversaries. We present experimental results with the NAO humanoid robots to illustrate the impact of our multi-humanoid world modeling approach. The challenges and approaches we present are relevant to the general problem of assessing and sharing information among multiple humanoid robots acting in a world with multiple types of objects.

Effective semi-autonomous telepresence

We investigate mobile telepresence robots to address the lack of mobility in traditional videoconferencing. To operate these robots, intuitive and powerful interfaces are needed. We present CoBot-2, an indoor mobile telepresence robot with autonomous capabilities, and a browser-based interface to control it. CoBot-2 and its web interface have been used extensively to remotely attend meetings and to guide local visitors to destinations in the building. From the web interface, users can control CoBot-2s camera, and drive with either directional commands, by clicking on a point on the floor of the camera image, or by clicking on a point in a map. We conduct a user study in which we examine preferences among the three control interfaces for novice users. The results suggest that the three control interfaces together cover well the control preferences of different users, and that users often prefer to use a combination of control interfaces. CoBot-2 also serves as a tour guide robot, and has been demonstrated to safely navigate through dense crowds in a long-term trial.

Online Pickup and Delivery Planning with Transfers for Mobile Robots

We have deployed a fleet of robots that pickup and deliver items requested by users in an office building. Users specify time windows in which the items should be picked up and delivered, and send in requests online. Our goal is to form a schedule which picks up and delivers the items as quickly as possible at the lowest cost. We introduce an auction-based scheduling algorithm which plans to transfer items between robots to make deliveries more efficiently. The algorithm can obey either hard or soft time constraints. We discuss how to replan in response to newly requested items, cancelled requests, delayed robots, and robot failures. We demonstrate the effectiveness of our approach through execution on robots, and examine the effect of transfers on large simulated problems.

Optimizing for Transfers in a Multi-vehicle Collection and Delivery Problem

We address the Collection and Delivery Problem (CDP) with multiple vehicles, such that each collects a set of items at different locations and delivers them to a dropoff point. The goal is to minimize either delivery time or the total distance traveled.We introduce an extension to the CDP: what if a vehicle can transfer items to another vehicle before making the final delivery? By dividing the labor among multiple vehicles, the delivery time and cost may be reduced. However, introducing transfers increases the number of feasible schedules exponentially. In this paper, we investigate this Collection and Delivery Problem with Transfers (CDP-T), discuss its theoretical underpinnings, and introduce a two-approximate polynomial time algorithm to minimize total distance travelled. Furthermore, we show that allowing transfers to take place at any location for the CDP-T results in at most a factor of two improvement. We demonstrate our approximation algorithms on large simulated problem instances. Finally, we deploy our algorithms on robots that transfer and deliver items autonomously in an office building.

Multi-observation sensor resetting localization with ambiguous landmarks

Successful approaches to the robot localization problem include particle filters, which estimate non-parametric localization belief distributions. Particle filters are successful at tracking a robot’s pose, although they fare poorly at determining the robot’s global pose. The global localization problem has been addressed for robots that sense unambiguous visual landmarks with sensor resetting, by performing sensor-based resampling when the robot is lost. Unfortunately, for robots that make sparse, ambiguous and noisy observations, standard sensor resetting places new pose hypotheses across a wide region, in poses that may be inconsistent with previous observations. We introduce multi-observation sensor resetting (MOSR) to address the localization problem with sparse, ambiguous and noisy observations. MOSR merges observations from multiple frames to generate new hypotheses more effectively. We demonstrate experimentally on the NAO humanoid robots that MOSR converges more efficiently to the robot’s true pose than standard sensor resetting, and is more robust to systematic vision errors.

Ridesharing with passenger transfers

Recently, ridesharing mobile applications, which dynamically match passengers to drivers, have begun to gain popularity. These services have the potential to fill empty seats in cars, reduce emissions and enable more efficient transportation. Ridesharing services become even more practical as robotic cars become available to do all the driving. In this work, we propose rideshare services which transfer passengers between multiple drivers. By planning for transfers, we can increase the availability and range of the rideshare service, and also reduce the total vehicular miles travelled by the network. We propose three heuristic algorithms to schedule rideshare routes with transfers. Each gives a tradeoff in terms of effectiveness and computational cost. We demonstrate these tradeoffs, both in simulation and on data from taxi passengers in San Francisco. We demonstrate scenarios where transferring passengers can provide a significant advantage.