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Rough Terrain Autonomous Mobility—Part 2: An Active Vision, Predictive Control Approach

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Abstract

Off-road autonomous navigation is one of the most difficult automation challenges from the point of view of constraints on mobility, speed of motion, lack of environmental structure, density of hazards, and typical lack of prior information. This paper describes an autonomous navigation software system for outdoor vehicles which includes perception, mapping, obstacle detection and avoidance, and goal seeking. It has been used on several vehicle testbeds including autonomous HMMWV's and planetary rover prototypes. To date, it has achieved speeds of 15 km/hr and excursions of 15 km.

We introduce algorithms for optimal processing and computational stabilization of range imagery for terrain mapping purposes. We formulate the problem of trajectory generation as one of predictive control searching trajectories expressed in command space. We also formulate the problem of goal arbitration in local autonomous mobility as an optimal control problem. We emphasize the modeling of vehicles in state space form. The resulting high fidelity models stabilize coordinated control of a high speed vehicle for both obstacle avoidance and goal seeking purposes. An intermediate predictive control layer is introduced between the typical high-level strategic or artificial intelligence layer and the typical low-level servo control layer. This layer incorporates some deliberation, and some environmental mapping as do deliberative AI planners, yet it also emphasizes the real-time aspects of the problem as do minimalist reactive architectures.

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Authors and Affiliations

  1. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 15213

    Alonzo Kelly & Anthony Stentz

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  1. Alonzo Kelly
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  2. Anthony Stentz
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Kelly, A., Stentz, A. Rough Terrain Autonomous Mobility—Part 2: An Active Vision, Predictive Control Approach. Autonomous Robots 5, 163–198 (1998). https://doi.org/10.1023/A:1008822205706

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