Service robotics, as it has been intended so far, views the accomplishment of a service mission mainly as the result of the action of a single robot. Swarm robotics tackles the very same problem from a different stance, i.e., as the result of a team effort of simple units. The project described here shows this particular approach. It defines first one simple unit (s-bot) capable of independently moving about on the ground and of dynamically establishing rigid or semi-rigid connections with other fellow units, and then it shows how a large group of them can, as a whole entity (swarm-bot), carry out a given task. Thanks to the ductility in assembling and forming its connections, a swarm-bot can readily cope with occasional failures of some components and promptly reshape the remaining swarm so as to replace the role of the failing units. Given such a plasticity, their possible applications is rather large ranging from harsh environment exploration to goods harvesting or goods transportation. At the moment, the project is at the stage of having defined a first simulating environment to be used both for the on-going hardware design and for the software control. The present paper describes this particular aspect of the project.

We overview our recent research on planetary mobility. Products of this effort include the Field Integrated Design & Operations rover (FIDO), Sample Return Rover (SRR), reconfigurable rover units that function as an All Terrain Explorer (ATE), and a multi-Robot Work Crew of closely cooperating rovers (RWC). FIDO rover is an advanced technology prototype; its design and field testing support NASA's development of long range, in situ Mars surface science missions. Complementing this, SRR implements autonomous visual recognition, navigation, rendezvous, and manipulation functions enabling small object pick-up, handling, and precision terminal docking to a Mars ascent vehicle for future Mars Sample Return. ATE implements on-board reconfiguration of rover geometry and control for adaptive response to adverse and changing terrain, e.g., traversal of steep, sandy slopes. RWC implements coordinated control of two rovers under closed loop kinematics and force constraints, e.g., transport of large payloads, as would occur in robotic colonies at future Mars outposts. RWC is based in a new extensible architecture for decentralized control of, and collective state estimation by multiple heterogeneous robotic platforms---CAMPOUT; we overview the key architectural features. We have conducted experiments with all these new rover system concepts over variable natural terrain. For each of the above developments, we summarize our approach, some of our key experimental results to date, and our future directions of planned development.

Future robotic vehicles will perform challenging tasks in rough terrain, such as planetary exploration and military missions. Rovers with actively articulated suspensions can improve rough-terrain mobility by repositioning their center of mass. This paper presents a method to control actively articulated suspensions to enhance rover tipover stability. A stability metric is defined using a quasi-static model, and optimized on-line. The method relies on estimation of wheel-terrain contact angles. An algorithm for estimating wheel-terrain contact angles from simple on-board sensors is developed. Simulation and experimental results are presented for the Jet Propulsion Laboratory Sample Return Rover that show the control method yields substantially improved stability in rough-terrain.

Outdoor mobile robot motion planning and navigation is a challenging problem in artificial intelligence. The search space density and dimensionality, system dynamics and environmental interaction complexity, and the perceptual horizon limitation all contribute to the difficultly of this problem. It is hard to generate a motion plan between arbitrary boundary states that considers sophisticated vehicle dynamics and all feasible actions for nontrivial mobile robot systems. Accomplishing these goals in real time is even more challenging because of dynamic environments and updating perception information. This thesis develops effective search spaces for mobile robot trajectory generation, motion planning, and navigation in complex environments. Complex environments are defined as worlds where locally optimal motion plans are numerous and where the sensitivity of the cost function is highly dependent on state and motion model fidelity. Examples include domains where obstacles are prevalent, terrain shape is varied, and the consideration of terramechanical effects is important. Three specific contributions are accomplished. First, a model-predictive trajectory generation technique is developed that numerically linearizes and inverts general predictive motion models to determine parameterized actions that satisfy the two-point boundary value problem. Applications on a number of mobile robot platforms (including skidsteered field robots, planetary rovers with actively articulating chassis, mobile manipulators, and autonomous automobiles)

Abstract- A system that enables continuous slip compensation for a Mars rover has been designed, implemented, and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage, and reach intended goals in high-slip environments. These components include: visual odometry, vehicle kinematics, a Kalman filter pose estimator, and a slip compensation/path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The vehicle kinematics for a rocker-bogie suspension system estimates motion by measuring wheel rates, and rocker, bogie, and steering angles. The Kalman filter merges data from an Inertial Measurement Unit (IMU) and visual odometry. This merged estimate is then compared to the kinematic estimate to determine how much slippage has occurred, taking into account estimate uncertainties. If slippage has occurred then a slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path. Index Terms – rover navigation, visual odometry, slip compensation, kalman filter, kinematics. I.

A long duration robotic presence on lunar and planetary surfaces will allow the acquisition of scientifically interesting information from a diverse set of surface and sub-surface sites. The wide range of terrain types including plains, cliffs, sand dunes, and lava tubes will require the development of robotic systems that can adapt to possibly rapidly changing terrain. These systems include single as well as teams of robots. In this paper, we describe the development of an integrated suite of autonomous, adaptive hardwarehoftware control methods called SMART Gystem for Mobility and- Access to Rough Terrain) that enables mobile robots to explore potentially important science sites currently beyond the reach of conventional rover designs. SMART uses the behavior coordination mechanisms of CAMPOUT, a previously developed system for multi-agent control. For the specific application area of cliffside exploration, SMART consists of a distributed sensing/mobility system for cooperative map-making called MITSAF (Model-based Information Theoretic- Sensing And Fusion) and rappelling down a cliff, moving to a designated way-point, and science sample acquisition from the cliff face. We also report the results of some experimental studies on highly sloped terrain and cliff faces.

A system that enables continuous slip compensation for a Mars rover has been designed, imple-mented, and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage, and reach intended goals in high-slip environments. These components include: visual odometry, vehicle kinematics, a Kalman filter pose estimator, and a slip-compensated path follower. Visual odometry tracks dis-tinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The kinematics for a rocker-bogie suspension system estimates vehicle motion by measuring wheel rates, and rocker, bogie, and steering angles. The Kalman filter processes mea-surements from an Inertial Measurement Unit (IMU) and visual odometry. The filter estimate is then compared to the kinematic estimate to determine whether slippage has occurred, taking into account estimate uncertainties. If slippage is detected, the slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.

Active Spoke System) is a novel locomotion system concept that utilizes rimless wheels with individually actuated spokes to provide the ability to step over large obstacles like legs, adapt to uneven surfaces like tracks, yet retaining the speed and simplicity of wheels. Since it lacks the complexity of legs and has a large effective (wheel) diameter, this highly adaptive system can move over extreme terrain with ease while maintaining respectable travel speeds. This paper presents the concept, preliminary kinematic analyses and design of an IMPASS based robot with two actuated spoke wheels and an articulated tail. The actuated spoke wheel concept allows multiple modes of motion, which give it the ability to assume a stable stance using three contact points per wheel, walk with static stability with two contact points per wheel, or stride quickly using one contact point per wheel. Straight-line motion and considerations for turning are discussed for the one- and two-point contact schemes followed by the preliminary design and recommendations for future study. Index Terms – IMPASS, rimless wheel, actuated spoke wheel, mobility, locomotion.

Future NASA’s missions include the search for past and existing life in the Universe and evidence on how the planets in the Solar system formed and evolved. In order to fulfill these goals sampling systems that meet the stringent requirements of the various environments are required to be developed. To support these objectives an ultrasonic/sonic driller/corer (USDC) device has been developed at Jet Propulsion Laboratory (JPL) to allow drilling and coring rocks for in-situ planetary analysis [1]. The site location and method of sampling are of vital importance to scientists. Surface rocks abrasion, small depth soil drilling, and deep drilling have been proposed. It has been suggested that another possible source of mineralogical or astrobiological information can be found by exploring the sidewall of canyons. The exploration of such sites requires the development of a limbed robotic system capable of walking and climbing slopes up to and including vertical faces and overhangs. An anchor/drilling mechanism is currently under development and is being installed on each leg of the four-legged Steep Terrain Access Robot (STAR). This paper presents the modeling, design, and preliminary testing results of the USDC for use as end-effectors of walking/climbing robots.

This chapter addresses computing strategies designed to enable field mobile robots to execute tasks requiring effective autonomous traversal of natural outdoor terrain. The primary focus is on computer vision-based perception and autonomous control. Hard computing methods are combined with apphed soft computing strategies in the context of three case studies associated with real-world robotics tasks including planetary surface exploration and land survey or reconnaissance. Each case study covers strategies implemented on wheeled robot research prototypes designed for field operations.