Path Planning for Reconfigurable Rovers in Planetary ... · 1. Introduction • Objectives – Autonomy on rovers ← Path Planning – Implementation of Path Planning Algorithm for

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UNIVERSITY OF MÁLAGA

Path Planning for Reconfigurable Roversin Planetary Exploration

Authors: J. Ricardo Sánchez 1

Carlos J. Pérez del Pulgar 1

Martin Azkarate 2

1 Universidad de Málaga2 European Space Agency

Outline

1. Introduction2. Simulation Scene3. Implementation4. Path Planning Algorithm5. Experiments6. Conclusions

Outline


1. Introduction

• ExoMars mission– Place a rover on the Martian surface by 2021– Find signs of current or past life on Mars– Kinematic Configuration of Rover: 6x6x6 Degrees Of Freedom– Can provide an extra mode of locomotion: Wheel-walking

• Improves traction on loose soils

1. Introduction

• Objectives– Autonomy on rovers ← Path Planning– Implementation of Path Planning Algorithm for reconfigurable rovers

• Previous approaches:– E. Rohmer et al. Dynamic simulation-based action planner for a

reconfigurable hybrid leg-wheel planetary exploration rover• Requirements:

– Find optimal path in terms of power consumption– Have into account driving and wheel-walking modes– Trajectories must be smooth and continuous

• Proposed Algorithm: Fast Marching– Use of a Simulation Environment to validate the algorithm

• Examples:– ARTEMIS: used to analyze dynamic contact of rovers with terrain

• Main advantage of using simulation software:– Can check algorithms prior to using real hardware– Saves money and time

• In our case:

Outline


2. Simulation Scene

• ExoTeR (ExoMars Testing Rover)• Kinematic Configuration:

– 6 Driving Joints• In charge of rolling wheels• Commanded in speed

– 4 Steering Joints• In charge of turning wheels• Commanded in position

– 6 Deployment Joints• In charge of deploying legs• Commanded in both position andspeed

• Virtual Model in V-REP:

2. Simulation Scene

• ExoTeR Locomotion ModesNormal driving• Walking Joints are locked• Maneuvers:

• Ackerman Turns• Spot Turns

Wheel-walking• Steering Joints are locked• Legs from one side drag the

rover while the others advance

2. Simulation Scene

• Martian Surface– Point cloud from Planetary Robotics Lab (PRL) at ESTEC– From point cloud to square grid with elevation data– Heightfield model in V-REP from grid– Texture from orthonormal image, modified to show two types of terrain

Loose Soil

Compact Soil

2. Simulation Scene

• Martian Surface– Each type of soil has different values of friction and slippage– Loose Terrain

• Best to use Wheel-walking– Compact Terrain

• Best to use Normal driving

TERRAIN INTERACTION

MODELING

2. Simulation Scene

• Cost Map– Detect Obstacles (high slopes and dangerous elements)– Make Obstacle Area Larger– Recognize soils– Add risk value

Outline


3. Implementation

• Simulation Environment Scheme– Matlab & Simulink: creation of data input

3. Implementation

• V-REP physics simulation• Use of multibody physics engine: VORTEX

– Lets use real values of parameters like dynamic friction, static friction and slippage

– Two axis → Adds behavior of lateral forces on wheels

Outline


4. Path Planning Algorithm

• Fast Marching Method• Wave Propagation → Potential Field• Gradient Descent Method → Path

Eikonal Equation in Power Consumption

Terms:

• W = Work• P = Power• vb = rover speed• R = Risk

• Initial Pose

• Alive Node

• Trial Node

• Far Node

4. Path Planning Algorithm

• Fast Marching Method• P/v+R is always a nonzero positive

value– Not local minimums

• For Reconfigurable Rovers:– min , , … , for n

locomotion modes• The locomotion mode with the least

value of P is the optimal one• The locomotion mode to use to reach at

each waypoint is decided interpolating the neighboring nodes

Eikonal Equation in Power Consumption

Terms:

• W = Work• P = Power• vb = rover speed• R = Risk

Wheel-walking Wheel-walking

Wheel-walking Driving

Outline


5. Experiments

• Obtain optimal paths in two cases:

Case 1: Only Driving• Cost to traverse loose

soil is too high

Case 2: Driving + Wheel-walking• Cost to traverse loose soil is

lower thanks to having wheel-walking available

x4x16

5. Experiments

• Case 1: Only drivingSteps• Setting Goal• Calculating path• Follow the trajectory

5. Experiments

• Case 2: Driving + Wheel-walking

x4x16x4x16x16x4x16

Steps• Setting Goal• Calculating path• Follow the trajectory

Outline


6. Conclusions

• Comparative between cases 1 and 2• A path planning that takes into account all available locomotion

modes in a reconfigurable rover is able to find even more optimal paths in certain cases.

• The use of Wheel-walking lets the rover find a better path in terms of power consumption

6. Conclusions

• In this work:– A Simulation Environment is created to validate path planning

algorithms– A path planning algorithm is implemented taking into account two

locomotion modes: normal driving and wheel-walking– Experiments demonstrate the advantages of having multiple

locomotion modes and a path planner like the one proposed

• Future work:– Consideration of slope in cost function– Optimal re-planning to adapt to mapping changes

DEPARTMENT OF SYSTEMS ENGINEERING AND AUTOMATIONUNIVERSITY OF MÁLAGA

Path Planning for Reconfigurable Roversin Planetary Exploration

Authors: J. Ricardo Sánchez Ibáñez

[email protected] Jesús Pérez del Pulgar Mancebo

[email protected] Azkarate

[email protected]

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Path Planning for Reconfigurable Rovers in Planetary ... · 1. Introduction • Objectives – Autonomy on rovers ← Path Planning – Implementation of Path Planning Algorithm for