For motion planning problems involving many or unbounded forms of uncertainty, it may not be possible to identify a path guaranteed to be feasible, requiring consideration of the trade-off between planner conservatism and the risk of infeasibility. This paper presents a novel real-time planning algorithm, chance constrained rapidly-exploring random trees (CC-RRT), which uses chance constraints to guarantee probabilistic feasibility for linear systems subject to process noise and/or uncertain, possibly dynamic obstacles. By using RRT, the algorithm enjoys the computational benefits of sampling-based algorithms, such as trajectory-wise constraint checking and incorporation of heuristics, while explicitly incorporating uncertainty within the formulation. Under the assumption of Gaussian noise, probabilistic feasibility at each time step can be established through simple simulation of the state conditional mean and the evaluation of linear constraints. Alternatively, a small amount of additional computation can be used to explicitly compute a less conservative probability bound at each time step. Simulation results show that this algorithm can be used for efficient identification and execution of probabilistically safe paths in real time.
This paper presents a real-time path planning algorithm that guarantees probabilistic feasibility for autonomous robots with uncertain dynamics operating amidst one or more dynamic obstacles with uncertain motion patterns. Planning safe trajectories under such conditions requires both accurate prediction and proper integration of future obstacle behavior within the planner. Given that available observation data is limited, the motion model must provide generalizable predictions that satisfy dynamic and environmental constraints, a limitation of existing approaches. This work presents a novel solution, named RR-GP, which builds a learned motion pattern model by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation. Obstacle trajectory GP predictions are conditioned on dynamically feasible paths identified from the reachability analysis, yielding more accurate predictions of future behavior. RR-GP predictions are integrated with a robust path planner, using chance-constrained RRT, to identify probabilistically feasible paths. Theoretical guarantees of probabilistic feasibility are shown for linear systems under Gaussian uncertainty; approximations for nonlinear dynamics and/or non-Gaussian uncertainty are also presented. Simulations demonstrate that, with this planner, an autonomous vehicle can safely navigate a complex environment in realtime while significantly reducing the risk of collisions with dynamic obstacles.
This paper introduces the Information-rich Rapidly-exploring Random Tree (IRRT), an extension of the RRT algorithm that embeds information collection as predicted using Fisher Information Matrices. The primary contribution of this algorithm is target-based information maximization in general (possibly heavily constrained) environments, with complex vehicle dynamic constraints and sensor limitations, including limited resolution and narrow field-of-view. An extension of IRRT for multi-agent missions is also presented. IRRT is distinguished from previous solutions strategies by its computational tractability and general constraint characterization. A progression of simulation results demonstrates that this implementation can generate complex target-tracking behaviors from a simple model of the trade-off between information gathering and goal arrival.
This paper presents a novel sampling-based planner, CC-RRT*, which generates robust, asymptotically optimal trajectories in real-time for linear Gaussian systems subject to process noise, localization error, and uncertain environmental constraints. CC-RRT* provides guaranteed probabilistic feasibility, both at each time step and along the entire trajectory, by using chance constraints to efficiently approximate the risk of constraint violation. This algorithm expands on existing results by utilizing the framework of RRT* to provide guarantees on asymptotic optimality of the lowest-cost probabilistically feasible path found. A novel riskbased objective function, shown to be admissible within RRT*, allows the user to trade-off between minimizing path duration and risk-averse behavior. This enables the modeling of soft risk constraints simultaneously with hard probabilistic feasibility bounds. Simulation results demonstrate that CC-RRT* can efficiently identify smooth, robust trajectories for a variety of uncertainty scenarios and dynamics.
Abstract-This paper considers the decision-making problem for a human-driven vehicle crossing a road intersection in the presence of other, potentially errant, drivers. Our approach relies on a novel threat assessment module, which combines an intention predictor based on support vector machines with an efficient threat assessor using rapidly-exploring random trees. This module warns the host driver with the computed threat level and the corresponding best "escape maneuver" through the intersection, if the threat is sufficiently large. Through experimental results with small autonomous and human-driven vehicles, we demonstrate that this threat assessment module can be used in real-time to minimize the risk of collision.
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