Fast Approximate Clearance Evaluation for Kinematically Constrained Articulated Suspension Systems

In this paper, we present a light-weight collision detection algorithm for motion planning of planetary rovers with articulated suspension systems. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated collision detection on all the candidate paths at a small interval. Solving the exact collision detection problem for articulated suspension systems requires simulating the vehicle settling on the terrain, which involves an inverse-kinematics problem with iterative nonlinear optimization under geometric constraints. However, such expensive computation is intractable for slow spacecraft computers, such as the RAD750 that is used by the Curiosity Mars rover and upcoming Mars 2020 rover. We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains conservative bounds on vehicle clearance, attitude, and suspension angles without iterative computation. It obtains those bounds by estimating the lowest and highest heights that each wheel may reach given the underlying terrain, and calculating the worst-case vehicle configuration associated with those extreme wheel heights. The bounds are guaranteed to be conservative, hence ensuring vehicle safety during autonomous navigation. ACE is planned to be used as part of the new onboard path planner of the Mars 2020 rover. This paper describes the algorithm in detail and validates our claim of conservatism and fast computation through experiments.

[1]  Alonzo Kelly,et al.  Optimal Rough Terrain Trajectory Generation for Wheeled Mobile Robots , 2007, Int. J. Robotics Res..

[2]  M. H. van Emden,et al.  Interval arithmetic: From principles to implementation , 2001, JACM.

[3]  G. Sohl,et al.  Characterization of the ROAMS Simulation Environment for Testing Rover Mobility on Sloped Terrain , 2009 .

[4]  Fiora Pirri,et al.  3D Mobility Learning and Regression of Articulated, Tracked Robotic Vehicles by Physics-based Optimization , 2012, VRIPHYS.

[5]  Jeng Yen,et al.  Sequence rehearsal and validation on surface operations of the Mars Exploration Rovers , 2004 .

[6]  M. Maimone,et al.  Chapter 3 SURFACE NAVIGATION AND MOBILITY INTELLIGENCE ON THE MARS EXPLORATION ROVERS , 2006 .

[7]  M. Golombek,et al.  Size‐frequency distributions of rocks on Mars and Earth analog sites: Implications for future landed missions , 1997 .

[8]  Jack Morrison,et al.  Driving on the surface of Mars with the rover sequencing and visualization program , 2005 .

[9]  John F. Mustard,et al.  Assessing the mineralogy of the watershed and fan deposits of the Jezero crater paleolake system, Mars , 2015 .

[10]  Larry H. Matthies,et al.  Terrain Adaptive Navigation for planetary rovers , 2009, J. Field Robotics.

[11]  A. Jain,et al.  Recent developments in the ROAMS planetary rover simulation environment , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[12]  Abhinandan Jain,et al.  ROAMS: planetary surface rover simulation environment , 2003 .