H.O.U.N.D.

A Low-Cost Platform for aggressive off-road autonomy research 

Elevation mapping for perceiving off-road environments

Rollover prevention system, tested in the real world 

Integrated with high-fidelity simulator

Abstract

Off-road autonomy, crucial for applications such as search-and-rescue, agriculture, and planetary exploration, poses unique problems due to challenging terrains, as well as due to the risk involved in testing or deploying such systems. Accessible platforms have the potential to widen the field to a broader set of researchers and students. Existing efforts in making on-road autonomy more accessible have seen success, yet aggressive off-road autonomy remains underserved. 

We seek to fill this gap by introducing HOUND, a 1/10th-scale, inexpensive, off-road autonomous car platform that can handle challenging outdoor terrains at high speeds. To aid development speed, we integrate HOUND with BeamNG, a state-of-the-art driving simulator to enable both software in the loop as well as hardware in the loop testing. To reduce the extent of ruggedization required, and thus cost, we integrate a rollover prevention system as a safety feature into the platform. Real-world trials over 50 kilometers demonstrate the platform's longevity and effectiveness over varied terrains and speeds.

hound_promo_final_copyright_free.mov

Note that the above video is a showcase of the platform. If all of the shots  in the video could have been  done in autonomous mode, there would not  be much left to do in off-road autonomy research

HOUND:

We provide an autonomy stack geared towards aggressive off-road autonomy. For the perception stack, we use elevation mapping with learning-based inpainting. For the high-level control, we integrate a Model Predictive Path Integral (MPPI) controller. We utilize low-cost sensors and take advantage of the Ardupilot ecosystem to handle intrinsic state estimation, hardware abstraction, and so on. The hardware is housed inside an enclosed plastic shell to protect it from environmental factors.

Simulator integration:

To aid development speed, we provide an integration with a state-of-the-art driving simulator, BeamNG, known for its crash simulation fidelity. The integration with the simulator allows both software-in-the-loop simulation and hardware-in-the-loop simulation. Software in the loop testing spoofs the perception system by providing the ground-truth elevation map and allows operating in a ROS-independent environment. Hardware in the loop simulation runs the entire autonomy stack as if it were running on the car, using ROS, with the hardware input-output being simulated by BeamNG.

Rollover Prevention System(RPS):

As rollovers are caused by inertial effects not felt outside the vehicle, they are much harder for an external operator to prevent. Unlike other works that may rely on physical ruggedization alone, we integrate a software-based rollover prevention system as a safety mechanism into the autonomy stack, to further reduce the cost of repairs.

Results:

Putting the RPS through it's paces in simulation:

We show the utility of the BeamNG simulator by using it to validate that rollover prevention system. We stress test the RPS in simulation, and find that it eliminates nearly all rollovers. On the left, we show the test with the RPS off, and on the right with the RPS on for the same scenarios.

Testing the stack in the real world:

We test the entire autonomy stack -- the physical hardware as well as the RPS, in both manual and autonomous modes, for 50 kilometers, over 4 terrains. We roll over 3 times and only damage a sacrificial part in the process. 

Appendix 

Design decisions

Heat management:

Dealing with EMI:

Preventing dust/dirt from getting in/on critical places:

Note that the other holes in the main body are for passing wires to-from the chassis, in which case the wires usually block water/dust from getting through, so no special considerations are made for those gaps.