  Many practical applications of robotics require systems that can operate
safely despite uncertainty. In the context of motion planning, two types of
uncertainty are particularly important when planning safe robot trajectories.
The first is environmental uncertainty -- uncertainty in the locations of
nearby obstacles, stemming from sensor noise or (in the case of obstacles'
future locations) prediction error. The second class of uncertainty is
uncertainty in the robots own state, typically caused by tracking or estimation
error. To achieve high levels of safety, it is necessary for robots to consider
both of these sources of uncertainty. In this paper, we propose a risk-bounded
trajectory optimization algorithm, known as Sequential Convex Optimization with
Risk Optimization (SCORA), to solve chance-constrained motion planning problems
despite both environmental uncertainty and tracking error. Through experiments
in simulation, we demonstrate that SCORA significantly outperforms
state-of-the-art risk-aware motion planners both in planning time and in the
safety of the resulting trajectories.
