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Off-Road Maneuverability

Typical robotic planning and control methods have assumed indoor or structured environments but cannot adapt to extreme terrains. We need to develop a principled continuous-state based decision making framework that allows mobile robots to plan and maneuver through extreme and complex terrains.

An important challenge for navigating robots in outdoor unstructured environments is the stochastic modeling of unexpected robot behaviors. Basically, the robots operating in real-world complex environments need to reason about the long-term results of their physical interactions with the environment, but due to the high complexity of the real world, it is generally impossible to predict future events in an accurate manner. For example, the effect of uneven road conditions or various disturbances on the robot's motion is hard to model (or learn from data) precisely. It is even more challenging to model the interaction between the robot and the environment, especially when the environment is dynamic. Other representative scenarios include drones flying with strong winds or submarines moving under ocean currents, where both air and water flows vary significantly in both space and time. Thus, it is necessary for the robots to consider these epistemic uncertainties caused by a lack of precise modeling of the environment while making decisions. We use Markov Decision Process as a basis to model autonomous decision-making under uncertainty problems. The solution to these problems is a closed-loop policy that maximizes a long-term goal and satisfies the safety constraints under a probabilistic interaction model between the robot and the environment. In principle, the resulting policy can generate a sequence of motor commands that complete the task assigned by a human, given that the probabilistic model can well describe the uncertainty of the world, and the computational method can allow the robot to calculate the policy within a reasonable amount of time.

Left: autonomous navigation in environments with elevation and obstacles.

However, many real-world problems are non-trivial, and obtaining the required probabilistic model of the world is generally impossible. Our research focuses on solving these two challenges by developing novel methods and leveraging strong computational power of GPUs. Our current focus is on addressing the computational part of the challenge by developing two planning algorithms that allow the robot to reason about its continuous motion on complicated terrain surfaces based on the kernel method (mesh-free) and finite-element method (mesh-based). Both methods leverage a set of discrete elements to represent the value function over the continuous space. The computation over the discrete parts can be parallelized, which allows our robot to reason and compute optimal policies in real-time to navigate through complicated terrains safely and efficiently.

In our recent results, we have tested in simulation with Mars surface and used a rover simulator to simulate the 2.5 D motion planning for slow-speedcomplex terrain traversability assessment. Left: Mars rover needs to make motion decisions in the navigable space with spatially varying terrestrial characteristics (cliffs, valleys, ridges, etc). This is different from the simplified and structured environments where there are only two types of representations, i.e., either obstacle-occupied or obstacle-free. Middle and Right: our preliminary result with three rovers exploring on NASA's Mars terrain model.

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Related Papers:

  • "Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes". Junhong Xu, Kai Yin, Lantao Liu. Robotics: Science and Systems (RSS). Virtual Conference, 2020.
  • "State-Continuity Approximation of Markov Decision Processes via Finite Element Analysis for Autonomous System Planning". Junhong Xu, Kai Yin, Lantao Liu. IEEE Robotics and Automation Letters (RA-L). 2020.