Robotic Resources
Contact Information

The Laboratory for Perceptual Robotics is committed to experimental research regarding robotics and autonomous, embedded systems. Our facilities for conducting have been assembled with the help of the National Science Foundation, DARPA, and the State of Massachusetts. Major components of the infrastructure are listed below.


Dexter- the UMass Bi-Manual Dexterity Platform

Dexter---the UMass Platform for Studying Bi-Manual Dexterity---consists of a BiSight stereo head with 4 mechanical and 3 optical degrees of freedom and four microphones for localizing and interpreting acoustic sources, two whole arm manipulators (WAMS) are each equipped with a three fingered Barrett hand with fingertip tactile load cells (ATI). Three VME cages host the computing system to control the integrated platform. Research is underway aimed at mechanisms for learning hierarchical control knowledge - categories of objects, activities, tasks, and situations - through a continuous interaction with the environment. We have developed techniques for exploiting visual and haptic guidance in grasping and manipulation tasks.

Mobile Platforms

There are a number of mobile platforms being used in the investigation of distributed robotic sensory systems, including eight custom designed UMass uBots and two iRobot ATRV series robots. The UMass uBot is a small two-wheeled robot equipped with multiple sensors, including a Cyclovision panoramic camera, eight Sharp GP2D12 IR proximity detectors, and acoustic microphones. The main processing unit is a 206 MHz StrongArm Computer. A K-Team Kameleon with an Robotics Extension Board equipped with a 22Mhz Motorola 68376 Processor is used for motion control. Communication with the uBots is made through an onboard Orinoco Wavelan card.

The ATRV series robots, manufactured by iRobot Co. are four-wheeled mobile platforms equipped with sonar sensors and wireless ethernet communications. LPR currently has an ATRV-Jr. and an ATRV-mini. We have attached a panoramic camera to the ATRV-Jr, and a Dell Inspiron 8200 laptop for control.

All of our mobile platforms are used in conjunction with the UMass Humanoid Torso and multiple pan-tilt-zoom cameras located around the lab to create an ubiquitous shared information network. These distributed resources are utilized cooperatively to solve multiple, simultaneous perceptual and motor tasks.

Quadruped Platform

Multi-legged locomotion platforms present similar challenges to those of multi-fingered robot hands when it comes to coordinated motion planning. Coordinated limb movements must serve both stability and mobility concerns by sequencing the movement of several independent kinematic chains. There are active communities considering gait synthesis for walking platforms and now for manipulation as well, but so far there has not emerged a unified framework for solving these problems. So we built Thing - a small, twelve degree of freedom, four legged walking robot - to generalize techniques designed for quasistatic finger gaits with dexterous robot hands to quasistatic locomotion gaits for quadruped robots.

Utah/MIT Hand

An integrated hand-arm system employs a GE-P50 robot to carry one of our two Utah/MIT hands and its actuator pack (click to enlarge). These hands have 4 fingers, each with 4 DOF actuated independently using 32 pneumatic actuators and antagonistic tendons. The compute architecture employs an analog controller for tendon management and a VME/VxWorks distributed controller. This platform has been used since 1991 to study collision-free reaching and grasping research.

Stanford/JPL Hand

Another GE-P50 carries our Stanford/JPL hand. This hand was developed by Ken Salisbury in the early 80's. It is a three fingered hand with three DOF per finger driven by an (n+1) tendon scheme. Instrumentation includes tendon tension sensors, motor position encoders, and Brock fingertip tactile sensors. The fingertip has a minimum force sensing capacity of 0.01 lbf. The controller is executed on a VME-based open torque control architecture. Our research on this platform includes haptic models of the dynamics in the phase portrait of the grasp formation process. We have been able to show that optimal policies can be learned that simultaneously acquire haptic models, estimate control state, and control grasp formation.


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