Designing ReachBot System Design Process with a Case Study of a Martian Lava Tube Mission Stephanie Newdick_2

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Designing ReachBot: System Design Process with a Case

Study of a Martian Lava Tube Mission

Stephanie Newdick

Dept. of Aero. & Astro.

Stanford University

496 Lomita Mall

Stanford, CA 94305

snewdick@stanford.edu

Tony G. Chen

Dept. of Mech. Engineering

Stanford University

440 Escondido Mall

Stanford, CA 94305

agchen@stanford.edu

Benjamin Hockman

Robotic Mobility Group

Jet Propulsion Laboratory

California Institute of Technology

Pasadena, CA 91109

benjamin.j.hockman@jpl.nasa.gov

Edward Schmerling

Dept. of Aero. & Astro.

Stanford University

496 Lomita Mall

Stanford, CA 94305

schmrlng@stanford.edu

Mark R. Cutkosky

Dept. of Mech. Engineering

Stanford University

440 Escondido Mall

Stanford, CA 94305

cutkosky@stanford.edu

Marco Pavone

Dept. of Aero. & Astro.

Stanford University

496 Lomita Mall

Stanford, CA 94305

pavone@stanford.edu

Abstract—In this paper we present a trade study-based method

to optimize the architecture of ReachBot, a new robotic concept

that uses deployable booms as prismatic joints for mobility in

environments with adverse gravity conditions and challenging

terrain. Speciﬁcally, we introduce a design process wherein we

analyze the compatibility of ReachBot’s design with its mission.

We incorporate terrain parameters and mission requirements to

produce a ﬁnal design optimized for mission-speciﬁc objectives.

ReachBot’s design parameters include (1) number of booms, (2)

positions and orientations of the booms on ReachBot’s chassis,

(3) boom maximum extension, (4) boom cross-sectional geome-

try, and (5) number of active/passive degrees-of-freedom at each

joint. Using ﬁrst-order approximations, we analyze the rela-

tionships between these parameters and various performance

metrics including stability, manipulability, and mechanical in-

terference. We apply our method to a mission where ReachBot

navigates and gathers data from a martian lava tube. The

resulting design is shown in Fig. 1.

TABLE OF CONTENTS

1. INTRODUCTION................................... 1

2. RELATED WORK .................................. 2

3. TRADE STUDY METHOD .......................... 2

4. CASE STUDY:MARTIAN LAVA TUBE EXPLO-

RATION ........................................... 5

5. CONCLUSION ..................................... 8

ACKNOWLEDGMENTS ............................... 8

REFERENCES ........................................ 8

BIOGRAPHY ......................................... 9

1. INTRODUCTION

The scientiﬁc interest in exploring novel environments re-

quires robots capable of versatile mobility in challenging and

unpredictable terrains. In particular, NASA’s interest in un-

derstanding the history of the solar system requires exploring

caves, cliffs, and other rocky terrain on planetary bodies like

Mars and the moon [1]. Additionally, the search for habitable

conditions and astrobiological signatures points to icy worlds

such as Enceladus and Europa as targets for further inves-

tigation [2]. These scientiﬁcally driven missions introduce

Figure 1: ReachBot exploring a martian lava tube. The ﬁnal

conﬁguration for this mission proﬁle has eight booms positioned at

the sides and top of the body with a leading LiDAR, trailing tether,

and science suite aimed at the surface below.

new challenges for robotic mobility. For example, navigating

caves and cliffs calls for a robot with anchored mobility under

adverse gravity, that is, where the robot must adhere to steep

or overhanging surfaces [3], but loose material, obstacles,

and crevasses in these environments make the anchor points

sparse and the terrain unpredictable. Additionally, features

on the surface of icy moons are largely unknown, reiterating

the importance of mobility in unpredictable terrain.

ReachBot is a new robot concept for versatile mobility in

challenging environments. Adverse gravity conditions and

unpredictable terrains expose a technological need in tradi-

tional robots: operations often require careful anchor or foot-

step placement, but the limited workspace and manipulation

capabilities of existing robot designs impede mobility in these

environments. As debuted in our past works [4–6], ReachBot

uses multiple deployable booms as prismatic joints to achieve

a large workspace which it leverages to navigate challenging

terrain. Through their lightweight, compactable structure,

arXiv:2210.11534v1 [cs.RO] 20 Oct 2022

these booms also reduce mass and complexity compared to

traditional rigid-link articulated-arm designs. ReachBot’s

large workspace increases the number of available anchor

points and accessible footsteps, a notable advantage when

navigating environments with sparse anchor points, obstruc-

tions, or large variations in terrain.

The combination of climbing or walking with long reach

makes the ReachBot paradigm amenable to a variety of differ-

ent environments, but the relative importance of design fea-

tures depends on the speciﬁc mission. For example, in mis-

sions with surrounding walls that enclose the robot, ReachBot

can act like a cable-driven robot by keeping its booms in

tension, requiring booms with strong tensile strength but

negligible compression requirements. Conversely, in a mis-

sion to navigate a gently curved surface, ReachBot acts as

a legged walker and demands that its booms are loaded in

compression. These different mission scenarios both take ad-

vantage of ReachBot’s long booms, but load them somewhat

differently, leading to different optimal conﬁgurations. More

generally, for any mission, we must balance operational pri-

ority, such as maximizing scientiﬁc value, with the technical

feasibility of a novel mobile manipulation platform.

Statement of Contributions: This paper presents a method

for designing ReachBot to provide access to previously in-

accessible space environments. The main contributions of

this work are threefold: (1) We introduce the conﬁgurable pa-

rameters for ReachBot, a robot concept that uses extendable

booms for mobility. (2) We present a method to quantify the

tradeoffs of a design by considering the robot conﬁguration,

terrain parameters, and mission requirements. (3) We present

a case study wherein we design ReachBot’s conﬁguration for

a mission to a martian lava tube.

Paper Organization: The rest of the paper is organized as

follows. In Section 2, we discuss existing trade studies for

mobile robot design and introduce a portfolio of past work

in dexterous manipulation that can be applied to ReachBot.

In Section 3, we describe the workﬂow of our design pro-

cess, including relevant parameters of the robot, terrain, and

mission. A highlight of this section is building a relationship

between ReachBot’s boom conﬁguration and various perfor-

mance metrics. Then, in Section 4, we apply this design

process to a case study exploring a martian lava tube. Finally,

we provide conclusions on the completeness of this process

and how it might be applied to missions distinct from the case

study where ReachBot would also excel.

2. RELATED WORK

Trade studies are widely used to develop robot designs that

maximize certain performance metrics by intelligently select-

ing the robots’ conﬁgurations in relation to their operational

design domains. For mobile robots, trade studies encompass

a wide range of parameters from high-level mobility mode [7]

down to joint orientation [8]. A valuable approach in de-

signing a mission-speciﬁc robot is to relate environmental,

task, and conﬁguration parameters to performance metrics,

then prioritize the performance metrics to align with mission

objectives [9].

While many existing trade studies for mobile robots consider

ground-based navigation metrics, such as a robot’s ability to

traverse soft soils or hard ground without loss of traction

(trafﬁcability), designing ReachBot requires extra attention

to performance criteria involving objects suspended by po-

tentially long limbs. As in past work, we approach this

problem by leveraging ReachBot’s similarities to a dexterous

manipulator: whereas a manipulator hand uses its ﬁngers to

push on an object, ReachBot uses its booms to pull on the

environment. From this perspective, we consider metrics

common in manipulator design that are relevant to Reach-

Bot’s performance such as stability, kinematic workspace,

and manipulability [10–12].

The stability of a grasped object is deﬁned by the maximum

external wrench (forces and torques) the grasp can resist in

its weakest direction. The eigenvalues of the grasp stiffness

matrix govern the grasp’s ability to apply or resist wrenches,

so its stability is determined by the minimum eigenvalue,

where the corresponding eigenvector denotes the weakest

direction. A grasp stiffness matrix with at least one negative

eigenvalue describes a grasp that is unstable with no external

applied wrench [13]. Conversely, a grasp’s wrench capability,

i.e. the maximum wrench it can apply, is deﬁned by the

maximum eigenvalue of the grasp stiffness matrix. The grasp

can apply this wrench in the direction of the corresponding

eigenvector.

The kinematic workspace of a grasp deﬁnes bounds of the

grasped object’s position and orientation corresponding to a

kinematically feasible conﬁguration of ﬁngers that does not

exceed joint limits [10]. Within this workspace, manipu-

lability is another measurement of the quality of a grasp.

Manipulability of a grasp can be quantiﬁed by the size of the

manipulability ellipsoid, which corresponds to the distance

from singularities (points where the grasped object’s move-

ment is limited) [14]. With multiple arms sharing the same

workspace, mechanical interference must be taken into con-

sideration as well [15]. Our approach in this paper combines

existing trade study methods for mobile robot design with

design optimization techniques for manipulators to optimize

ReachBot’s conﬁguration for its mission.

3. TRADE STUDY METHOD

In this section, we detail each step of our design process

and deﬁne both conﬁgurable and ﬁxed parameters. Fig. 2

shows the process overview for designing ReachBot in known

terrain. First, we analyze the workspace and mechanical

interactions of the robot and terrain. Then, incorporating

mission requirements, we compute quantitative performance

metrics and ﬁnalize the design. Because the exact terrain

details may not be known during the design process, we

use a Monte Carlo method to randomize terrain parameters,

then design ReachBot to perform well over the portfolio of

possible terrains. In this way, our design process focuses on

maintaining versatility in unknown environments.

Figure 2: Designing ReachBot for a given mission proceeds from

the interaction between the robot and terrain, then incorporates mis-

sion objectives to evaluate performance and deliver a ﬁnal design.

Section 3A describes basis geometries and properties of

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DesigningReachBot:SystemDesignProcesswithaCaseStudyofaMartianLavaTubeMissionStephanieNewdickDept.ofAero.&Astro.StanfordUniversity496LomitaMallStanford,CA94305snewdick@stanford.eduTonyG.ChenDept.ofMech.EngineeringStanfordUniversity440EscondidoMallStanford,CA94305agchen@stanford.eduBenjaminHockmanRobo...

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Designing ReachBot System Design Process with a Case Study of a Martian Lava Tube Mission Stephanie Newdick_2

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