Abstract-Existing high-dimensional motion planning algorithms are simultaneously overpowered and underpowered. In domains sparsely populated by obstacles, the heuristics used by sampling-based planners to navigate "narrow passages" can be needlessly complex; furthermore, additional post-processing is required to remove the jerky or extraneous motions from the paths that such planners generate. In this paper, we present CHOMP, a novel method for continuous path refinement that uses covariant gradient techniques to improve the quality of sampled trajectories. Our optimization technique converges over a wider range of input paths and is able to optimize higherorder dynamics of trajectories than previous path optimization strategies. As a result, CHOMP can be used as a standalone motion planner in many real-world planning queries. The effectiveness of our proposed method is demonstrated in manipulation planning for a 6-DOF robotic arm as well as in trajectory generation for a walking quadruped robot.
In this paper, we present CHOMP (Covariant Hamiltonian Optimization for Motion Planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. CHOMP can be used to locally optimize feasible trajectories, as well as to solve motion planning queries, converging to lowcost trajectories even when initialized with infeasible ones. It uses Hamiltonian Monte Carlo to alleviate the problem of convergence to high-cost local minima (and for probabilistic completeness), and is capable of respecting hard constraints along the trajectory. We present extensive experiments with CHOMP on manipulation and locomotion tasks, using 7-DOF manipulators and a rough-terrain quadruped robot.
Abstract-The Rapidly-exploring Random Tree (RRT) algorithm has found widespread use in the field of robot motion planning because it provides a single-shot, probabilistically complete planning method which generalizes well to a variety of problem domains. We present the Multipartite RRT (MP-RRT), an RRT variant which supports planning in unknown or dynamic environments. By purposefully biasing the sampling distribution and re-using branches from previous planning iterations, MP-RRT combines the strengths of existing adaptations of RRT for dynamic motion planning. Experimental results show MP-RRT to be very effective for planning in dynamic environments with unknown moving obstacles, replanning in high-dimensional configuration spaces, and replanning for systems with spacetime constraints.
We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and "certificates" that ensure the output of an abstract high-level planner can be realized by lower layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of rough terrains. Other novel aspects of our past research efforts include a variety of pioneering inverse optimal control techniques as well as a system for planning using arbitrary pre-recorded robot behaviors.
Abstract-We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and "certificates" that ensure the output of an abstract highlevel planner can be realized by deeper layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of rough terrains.
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