Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

Using Dynamic Analysis for Realistic Animation of Articulated Bodies

Abstract: A major problem in computer animation is creating motion that appears natural and realistic, particularly in such complex articulated bodies as humans and other animals. At present, truly lifelike motion is produced mainly by copying recorded images, a tedious and lengthy process that requires considerable external equipment. An alternative is the use of dynamic analysis to predict realistic motion. Using dynamic motion control, bodies are treated as masses acting under the influence of external and internal forces and torques. Dynamic control is advantageous because motion is naturally restricted to physically realizable patterns, and many types of motion can be predicted automatically. Use of dynamics is computationally expensive and specifying controlling forces and torques can be difficult. However, there is evidence that dynamics offers hope for more realistic, natural, and automatic motion control. Because such motion simulates real world conditions, an animation system using dynamic analysis is also a useful tool in such related fields as robotics and biomechanics.

FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning.

Abstract: Fast and safe navigation of dynamical systems through a priori unknown cluttered environments is vital to many applications of autonomous systems. However, trajectory planning for autonomous systems is computationally intensive, often requiring simplified dynamics that sacrifice safety and dynamic feasibility in order to plan efficiently. Conversely, safe trajectories can be computed using more sophisticated dynamic models, but this is typically too slow to be used for real-time planning. We propose a new algorithm FaSTrack: Fast and Safe Tracking for High Dimensional systems. A path or trajectory planner using simplified dynamics to plan quickly can be incorporated into the FaSTrack framework, which provides a safety controller for the vehicle along with a guaranteed tracking error bound. This bound captures all possible deviations due to high dimensional dynamics and external disturbances. Note that FaSTrack is modular and can be used with most current path or trajectory planners. We demonstrate this framework using a 10D nonlinear quadrotor model tracking a 3D path obtained from an RRT planner.

A general formal framework for pathfinding problems with multiple agents

Abstract: Pathfinding for a single agent is the problem of planning a route from an initial location to a goal location in an environment, going around obstacles Pathfinding for multiple agents also aims to plan such routes for each agent, subject to different constraints, such as restrictions on the length of each path or on the total length of paths, no self-intersecting paths, no intersection of paths/plans, no crossing/meeting each other It also has variations for finding optimal solutions, eg, with respect to the maximum path length, or the sum of plan lengths These problems are important for many real-life applications, such as motion planning, vehicle routing, environmental monitoring, patrolling, computer games Motivated by such applications, we introduce a formal framework that is general enough to address all these problems: we use the expressive high-level representation formalism and efficient solvers of the declarative programming paradigm Answer Set Programming We also introduce heuristics to improve the computational efficiency and/or solution quality We show the applicability and usefulness of our framework by experiments, with randomly generated problem instances on a grid, on a real-world road network, and on a real computer game terrain

Three Dimensional Receding Horizon Control for UAVs

Abstract: This paper presents a receding horizon controller (RHC) that can be used to design trajectories for an aerial vehicle flying through a three dimensional terrain with obstacles and no-fly zones. To avoid exposure to threats, the paths are chosen to stay as close to the terrain as possible, but the vehicle can choose to pop-up over the obstacles if necessary. The approach is similar to our previous two-dimensional algorithms that construct a coarse cost map to provide approximate paths from a sparse set of nodes to the goal and then use Mixed-integer Linear Programming (MILP) optimization to design a detailed trajectory. The main contribution of this paper is to extend this approach to 3D, in particular providing a new algorithm for connecting the cost map and the detailed path in the MILP. This connection is done by introducing a new cost-to-go function that includes an altitude penalty and accounts for the vehicle dynamics. Initial guess for MILP RHC is constructed from the previous solution and is shown to reduce the solution time. Several simulation results are presented to show that the path planning algorithm yields good overall performance and is computationally tractable in a complex environment.