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Abstract
This paper presents a novel approach to deal with chance constrained navigation, i.e., a problem where a robot has to plan its path among obstacles in the presence of noise and perturbations, subject to a maximum probability of failure. An improved formulation allows the problem to be framed as a single stage optimization using the MILP-MPC framework through the use of a piecewise linear approximation of the non-linear function that represent the chance constraints. Time is included in the optimization to enable more efficient risk allocation. A comparison with state-of-the-art algorithm shows advantages of the proposed approach.
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