John R. Spletzer

A vision-based formation control framework

The invention relates to a rack for electronic plug-in units, comprising a backplane. The backplane comprises at least one connector to which a connector provided in the electronic plug-in unit connects when the plug-in unit is pushed into the rack. The backplane is attached to the rack with a fastener made of a resilient material. A moment arm is formed between a point which attaches the fastener to the rack and the backplane. When the plug-in unit is pushed into the rack, there is a tolerance for alignment of the connectors enabling connection of the connectors. Furthermore, when the plug-in unit is in the rack, the mobility of the backplane prevents the connectors and/or the backplane from breaking as the rack moves.

Distributed Search and Rescue with Robot and Sensor Teams

We develop a network of distributed mobile sensor systems as a solution to the emergency response problem. The mobile sensors are inside a building and they form a connected ad-hoc network. We discuss cooperative localization algorithms for these nodes. The sensors collect temperature data and run a distributed algorithm to assemble a temperature gradient. The mobile nodes are controlled to navigate using this temperature gradient. We also discuss how such networks can assist human users to find an exit. We have conducted an experiment to at a facility used to train firefighters to understand the environment and to test component technology. Results from experiments at this facility as well as simulations are presented here.

Real-time vision-based control of a nonholonomic mobile robot

This paper considers the problem of vision-based control of a nonholonomic mobile robot. We describe the design and implementation of real-time estimation and control algorithms on a car-like robot platform using a single omni-directional camera as a sensor without explicit use of odometry. We provide experimental results for each of these vision-based control objects. The algorithms are packaged as control modes and can be combined hierarchically to perform higher level tasks involving multiple robots.

A Framework and Architecture for Multi-Robot Coordination

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment, with applications ranging from scouting and reconnaissance, to search and rescue, to manipulation tasks, to cooperative localization and mapping, and formation control. Our software framework allows a modular and hierarchical approach to programming deliberative and reactive behaviors in autonomous operation. Formal definitions for sequential composition, hierarchical composition, and parallel composition allow the bottom-up development of complex software systems. We demonstrate the algorithms and software on an experimental testbed that involves a group of carlike robots, each using a single omnidirectional camera as a sensor without explicit use of odometry.

Ad hoc networks for localization and control

We consider a team of mobile robots equipped with sensors and wireless network cards and the task of navigating to a desired location in a formation. We develop a set of algorithms for (a) discovery; (b) cooperative localization; and (c) cooperative control. Discovery involves the use of sensory information to organize the robots into a mobile network allowing each robot to establish its neighbors and, when necessary, one or more leaders. Cooperative control is the task of achieving a desired goal position and orientation and desired formation shape and maintaining it. Cooperative localization allows each robot to estimate its relative position and orientation with respect to its neighbors and hence the formation shape. We show numerical results and simulations for a team of nonholonomic, wheeled robots with omnidirectional cameras sharing a wireless communication network.

Convex Optimization Strategies for Coordinating Large-Scale Robot Formations

This paper investigates convex optimization strategies for coordinating a large-scale team of fully actuated mobile robots. Our primary motivation is both algorithm scalability as well as real-time performance. To accomplish this, we employ a formal definition from shape analysis for formation representation and repose the motion planning problem to one of changing (or maintaining) the shape of the formation. We then show that optimal solutions, minimizing either the total distance or minimax distance the nodes must travel, can be achieved through second-order cone programming techniques. We further prove a theoretical complexity for the shape problem of O(m1.5) as well as O(m) complexity in practice, where m denotes the number of robots in the shape configuration. Solutions for large-scale teams (1000s of robots) can be calculated in real time on a standard desktop PC. Extensions integrating both workspace and vehicle motion constraints are also presented with similar complexity bounds. We expect these results can be generalized for additional motion planning tasks, and will prove useful for improving the performance and extending the mission lives of large-scale robot formations as well as mobile ad hoc networks.

A bounded uncertainty approach to multi-robot localization

We offer a new approach to the multi-robot localization problem. Using an unknown-but-bounded model for sensor error, we are able to define convex polytopes in the configuration space of the robot team that represent the set of configurations consistent with all sensor measurements. Estimates for the uncertainty in various parameters of the teams configuration such as the absolute position of a single robot, or the relative positions of two or more nodes can be obtained by projecting this polytope onto appropriately chosen subspaces of the configuration space. We propose a novel approach to approximating these projections using linear programming techniques. The approach can handle both bearing and range measurements with a computational complexity scaling polynomially in the number of roots. Finally, the workload is readily distributed - requiring only the communication of sensor measurements between robots. We provide simulation results for this approach implemented on a multi-robot team.

Cooperative Transport of Planar Objects by Multiple Mobile Robots Using Object Closure

This paper addresses the problem of transporting objects by multiple mobile robots using the concept of object closure. In contrast to other manipulation techniques that are typically derived from form or force closure constraints, object closure requires the less stringent condition that the object be trapped or caged by the robots. We present experimental results that show car-like robots controlled using visual feedback, transporting an object in an obstacle free environment toward a prescribed goal.

On-line calibration of multiple LIDARs on a mobile vehicle platform

In this paper, we examine the problem of extrinsic calibration of multiple LIDARs on a mobile vehicle platform. To achieve fully automated and on-line calibration, the original non-linear calibration model is reformulated as a second-order cone program (SOCP). This provides an advantage over more standard linearized approaches in that a priori information such as a default LIDAR calibration, calibration tolerances, etc., can be readily modeled. Furthermore, in contrast to general non-linear methods, the SOCP relaxation is convex, returns a global minimum, and can be solved very quickly using modern interior point methods (IPM). This enables the calibration to be estimated on-line for multiple LIDARs simultaneously. Experimental results are provided where the approach is used to successfully calibrate a pair of Sick LMS291-S14 LIDARs mounted on a mobile vehicle platform. These showed the SOCP formulation yielded a more accurate reconstruction and was 1–2 orders of magnitude faster than the traditional non-linear least-squares approach.

On the deployment of a hybrid free-space optic/radio frequency (FSO/RF) mobile ad-hoc network

The hybrid free-space optics/radio frequency (FSO/RF) network paradigm promises new levels of throughput for sensor and mobile ad-hoc networks. However, several challenges must be addressed before such a network model can be realized. These include a means by which deployed robots can autonomously establish optical links over sufficiently long distances as well as the formulation of a mobile network architecture that can exploit high throughput FSO channels. In this paper, we offer solutions to these problems in the form of hierarchical link acquisition and routing protocols. The heart of our link acquisition system (LAS) is a vision-based alignment phase for locating robot link partners via high zoom camera systems. Identification is accomplished in real-time using a multi-resolution image representation and normalized intensity distribution (NID) as a similarity metric. Our routing protocol relies upon the hierarchical state routing (HSR) model adapted to the FSO/RF paradigm. Experimental results from a large set of link acquisition trials as well as a small scale FSO/RF deployment are provided to support our approach.