Aveek K. Das

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.

Hybrid control of formations of robots

We describe a framework for controlling a group of nonholonomic mobile robots equipped with range sensors. The vehicles are required to follow a prescribed trajectory while maintaining a desired formation. By using the leader-following approach, we formulate the formation control problem as a hybrid (mode switching) control system. We then develop a decision module that allows the robots to automatically switch between continuous-state control laws to achieve a desired formation shape. The stability properties of the closed-loop hybrid system are studied using the Lyapunov theory. We do not use explicit communication between robots; instead we integrate optimal estimation techniques with nonlinear controllers. Simulation and experimental results verify the validity of our approach.

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.

COOPERATIVE CONTROL OF ROBOT FORMATIONS

We describe a framework for controlling and coordinating a group of nonholonomic mobile robots equipped with range sensors, with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. We derive control algorithms that allow the robots to control their position and orientation with respect to neighboring robots or obstacles in the environment. We then outline a coordination protocol that automatically switches between the control laws to maintain a specified formation. Two simple trajectory generators are derived from potential field theory. The first allows each robot to plan its reference trajectory based on the information available to it. The second scheme requires sharing of information and enables a rigid group formation. Numerical simulations illustrate the application of these ideas and demonstrate the scalability of the proposed framework for a large group of robots.

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.

Formation control with configuration space constraints

We address the problem of controlling a team of robots subject to constraints on relative positions. We adopt the general framework of leader-follower control in which a network of controllers is used to control the position and orientation of the team and its shape. We propose two improvements to this scheme. First, we introduce cooperative leader-following where the motion of a robot is determined not only by its leader, but also by other robots including their followers. Second, we allow constraints that are induced by limitations on ranges of sensors and wireless network cards. Our approach is based on potential field controllers for each robot and the on-line modification of these controllers to accommodate motion constraints induced by other robots in the group. We present experimental results with a team of three car-like robots equipped with omnidirectional cameras and 802.11b network cards.

A modular architecture for formation control

We present a four layer architecture for coordinating a team of mobile robots equipped with range sensors and wireless network cards, and the task of navigating to a desired location in a desired formation. We develop graph-based algorithms for discovery, and 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 formation shape and maintaining it. In addition, we integrate a modular adaptive controller into our framework to explicitly deal with the unknown dynamics of the vehicles information. Numerical simulations illustrate the application of these ideas and demonstrate the scalability of the proposed architecture for a large group of robots.

Control Graphs for Robot Networks

In this paper we address the problem of stabilizing a group of mobile robots information. The group is required to follow a prescribed trajectory, while achieving and maintaining a desired formation. We describe algorithms for assigning control policies to different robots, based on sensor and actuator constraints. This assignment is described by a control graph. We relate the structure of the control graph to the stability of the dynamics of the formation. We examine both holonomic and nonholonomic mobile robot formations, and present analytical results and numerical simulations illustrating our approach.