[Robotics Institute Thesis Proposal ] Adaptive Model-Predictive Motion Planning for Navigation in Complex Environments

Author:
Thomas Howard
Robotics Institute
Carnegie Mellon University

Abstract:
Outdoor mobile robot motion planning and navigation is a challenging problem in robot autonomy because of the dimensionality of the search space, the complexity of the system dynamics and the environmental interaction, and the typically limited perceptual horizon. In general, it is intractable to generate a motion plan between arbitrary boundary states that consider sophisticated models of vehicle dynamics and the entire set of feasible actions for nontrivial systems. It is even more difficult to accomplish the aforementioned goals in real time, which is necessary due to dynamic environments and updated perceptual information.

In this proposal, complex environments are defined as worlds where locally optimal motion plans are numerous and where the sensitivity of the cost function is highly dependent on state and mobility model fidelity. Examples of these include domains where obstacles are prevalent, terrain shape is varied, and the consideration of terramechanical models is important. Sequential search processes provide globally optimal solutions but are constrained to search only edges that exist in the graph and satisfy state constraints in the discretized representation of the world. Optimization and relaxation techniques determine only locally optimal, possibly homotopically distinct trajectories and it can be difficult to provide good initial guesses of solutions. Such techniques are arguably more informed and efficient as they follow the gradients of the cost functions to optimize trajectories and can satisfy boundary state constraints in the continuum. A better solution is to leverage the benefits of each approach and to apply it in a hybrid optimization method, relaxing local and regional motion planning sequential search spaces to improve relative optimality of solutions. Relative optimality is defined as the relationship between the quality of a motion plan and the amount of effort (time, computational resources, etc...) required to produce it. In order to achieve this, real-time processes for informed action generation (production of trajectories that consider sophisticated models of motion, suspension, and interaction with the environment) at the regional motion planning level to initialize the optimization must be developed. Since the optimality of executed path can directly correlated to fidelity of the motion model, a related issue is that of system identification, the adaptation of vehicle models using state and sensor data to model predictable disturbances.

In this thesis, I propose to develop techniques to generate feasible motion plans at the local and regional levels that consider sophisticated dynamics models, wheel-terrain interaction, and vehicle configuration to improve navigation capabilities of mobile robots operating in complex environments. The proposed work approaches this problem through developing, applying, and characterizing the benefits of four distinct extensions of work in model-predictive motion planning. The first is the development of a hybrid optimization technique that considers informed mobility models to improve the relative optimality of motion plans in complex environments. The second involves the optimization of search spaces through relaxation of edges and nodes. The third and fourth extensions involve the development of methods for real-time informed action generation that considers varying mobility models and simultaneous model identification and control to tune the predictive motion models. All of this work is in line with the greater goal of developing mobile robot motion planners that effectively navigate in complex environments while considering relative optimality of actions. The application of such techniques may resolve many undesirable behaviors of real systems, leading to mobile robots that are more efficient, robust, and effective at performing tasks in the real world.


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