Abstract-Exploration of high risk terrain areas such as cliff faces and site construction operations by autonomous robotic systems on Mars requires a control architecture that is able to autonomously adapt to uncertainties in knowledge of the environment. We report on the development of a software/hardware framework for cooperating multiple robots performing such tightly coordinated tasks. This work builds on our earlier research into autonomous planetary rovers and robot arms. Here, we seek to closely coordinate the mobility and manipulation of multiple robots to perform examples of a cliff traverse for science data acquisition, and site construction operations including grasping, hoisting, and transport of extended objects such as large array sensors over natural, unpredictable terrain. In support of this work we have developed an enabling distributed control architecture called control architecture for multirobot planetary outposts (CAMPOUT) wherein integrated multirobot mobility and control mechanisms are derived as group compositions and coordination of more basic behaviors under a task-level multiagent planner. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. In this paper, we describe CAMPOUT, and its application to ongoing physical experiments with multirobot systems at the Jet Propulsion Laboratory in Pasadena, CA, for exploration of cliff faces and deployment of extended payloads.

A rock-climbing robot is presented that can free climb on vertical, overhanging, and inverted rock faces. This type of system has applications to extreme terrain on Mars or for sustained mobility on microgravity bodies. The robot grips the rock using hierarchical arrays of microspines. Microspines are compliant mechanisms made of sharp hooks and flexible elements that allow the hooks to move independently and opportunistically grasp roughness on the surface of a rock. This paper presents many improvements to early microspine grippers, and the application of these new grippers to a four-limbed robotic system, LEMUR IIB. Each gripper has over 250 microspines distributed in 16 carriages. Carriages also move independently with compliance to conform to larger, cm-scale roughness. Single gripper pull testing on a variety of rock types is presented, and on rough rocks, a single gripper can support the entire mass of the robot (10 kg) in any orientation. Several sensor combinations for the grippers were evaluated using a smaller test-gripper. Rock-climbing mobility experiments are also described for three characteristic gravitational orientations. Finally, a sample acquisition tool that uses one of the robot's grippers to enable rotary percussive drilling is shown. C 2013 Wiley Periodicals, Inc.

This paper describes a stereo vision-based system for autonomous navigation in maritime environments. The system consists of two key components. The Hammerhead vision system detects geometric hazards (i.e., objects above the waterline) and generates both grid-based hazard maps and discrete contact lists (objects with position and velocity). The R4SA (robust, real-time, reconfigurable, robotic system architecture) control system uses these inputs to implement sensor-based navigation behaviors, including static obstacle avoidance and dynamic target following. As far as the published literature is concerned, this stereo vision-based system is the first fielded system that is tailored for high-speed, autonomous maritime operation on smaller boats. In this paper, we present a description and experimental analysis of the Hammerhead vision system, along with key elements of the R4SA control system. We describe the integration of these systems onto a number of high-speed unmanned surface vessels and present experimental results for the combined vision-based navigation system. C

Abstract:We review computational intelligence methods of sensory perception and cognitive functions in animals, humans, and artificial devices. Top-down symbolic methods and bottom-up sub-symbolic approaches are described. In recent years, computational intelligence, cognitive science and neuroscience have achieved a level of maturity that allows integration of top-down and bottom-up approaches in modeling the brain. Continuous adaptation and learning is a key component of computationally intelligent devices, which is achieved using dynamic models of cognition and consciousness. Human cognition performs a granulation of the seemingly homogeneous temporal sequences of perceptual experiences into meaningful and comprehensible chunks of concepts and complex behavioral schemas. They are accessed during action selection and conscious decision making as part of the intentional cognitive cycle. Implementations in computational and robotic environments are demonstrated.

PFS (Prime Focus Spectrograph), a next generation facility instrument on the 8.2-meter Subaru Telescope, is a very wide-field, massively multiplexed, optical and near-infrared spectrograph. Exploiting the Subaru prime focus, 2394 reconfigurable fibers will be distributed over the 1.3 deg field of view. The spectrograph has been designed with 3 arms of blue, red, and near-infrared cameras to simultaneously observe spectra from 380nm to 1260nm in one exposure at a resolution of ∼1.6−2.7Å. An international collaboration is developing this instrument under the initiative of Kavli IPMU. The project is now going into the construction phase aiming at undertaking system integration in 2017-2018 and subsequently carrying out engineering operations in 2018-2019. This article gives an overview of the instrument, current project status and future paths forward.