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nDEPARTMENT OF SYSTEMS ENGINEERING AND AUTOMATION

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

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

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

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

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

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

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

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

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

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

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

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

ricardosan@uma.esCarlos Jesús Pérez del Pulgar Mancebo

carlosperez@uma.esMartin Azkarate

Martin.Azkarate@esa.int