We present a contact planner for complex legged locomotion tasks: standing up, climbing stairs using a handrail, crossing rubble and getting out of a car. The need for such a planner was shown at the Darpa Robotics Challenge, where such behaviors could not be demonstrated (except for egress). Current planners suffer from their prohibitive algorithmic complexity, because they deploy a tree of robot configurations projected in contact with the environment. We tackle this issue by introducing a reduction property: the reachability condition. This condition defines a geometric approximation of the contact manifold, which is of low dimension, presents a Cartesian topology, and can be efficiently sampled and explored. The hard contact planning problem can then be decomposed into two sub-problems: first, we plan a path for the root without considering the whole-body configuration, using a sampling-based algorithm; then, we generate a discrete sequence of whole-body configurations in static equilibrium along this path, using a deterministic contact-selection algorithm. The reduction breaks the algorithm complexity encountered in previous works, resulting in the first interactive implementation of a contact planner (open source). While no contact planner has yet been proposed with theoretical completeness, we empirically show the interest of our framework: in a few seconds, with high success rates, we generate complex contact plans for various scenarios and two robots, HRP-2 and HyQ. These plans are validated either in dynamic simulations, or on the real HRP-2 robot.

Multiped locomotion in cluttered environments is addressed as the problem of planning acyclic sequences of contacts, that characterize the motion. In order to overcome the inherent combinatorial difficulty of the problem, we separate it in two subproblems: first, planning a guide trajectory for the root of the robot and then, generating relevant contacts along this trajectory. This paper proposes theoretical contributions to these two subproblems. We propose a theoretical characterization of the guide trajectory, named "true feasibility", which guarantee that a guide can be mapped into the contact manifold of the robot. As opposed to previous approaches, this property makes it possible to assert the relevance of a guide trajectory without explicitly computing contact configurations. This property can be efficiently checked by a sample-based planner (e.g. we implemented a visibility PRM). Since the guide trajectories that we characterized are easily mapped to a valid sequence of contacts, we then focused on how to select a particular sequence with desirable properties, such as robustness, efficiency and naturalness, only considered for cyclic locomotion so far. Based on these novel theoretical developments, we implemented a complete acyclic contact planner and demonstrate its efficiency by producing a large variety of movements with three very different robots (humanoid, insectoid, dexterous hand) in five challenging scenarios. The planner is very efficient in quality of the produced movements and in computation time: given a computed RB-PRM, a legged figure or a dexterous hand can generate its motion in real time. This result outperforms any previous acyclic contact planner.

We present a novel approach to perform probabilistic collision detection between a high-DOF robot and high-DOF obstacles in dynamic, uncertain environments. In dynamic environments with a high-DOF robot and moving obstacles, our approach efficiently computes accurate collision probability between the robot and obstacles with upper error bounds. Furthermore, we describe a prediction algorithm for future obstacle position and motion that accounts for both spatial and temporal uncertainties. We present a trajectory optimization algorithm for high-DOF robots in dynamic, uncertain environments based on probabilistic collision detection. We highlight motion planning performance in challenging scenarios with robot arms operating in environments with dynamically moving human obstacles.

We present a novel algorithm to compute collision free trajectories in dynamic environments. Our approach is general and does not require a priori knowledge about the obstacles or their motion. We use a replanning framework that interleaves optimization-based planning with execution.Furthermore, we describe a parallel formulation that exploits a high number of cores on commodity graphics processors (GPUs) to compute a high-quality path in a given time interval.We derive bounds on how parallelization can improve the responsiveness of the planner and the quality of the trajectory. I. INT RODUCTIONRobots are increasingly used in dynamic or time-varying environments. These scenarios are composed of moving obstacles, and it is important to compute collision-free trajectories for navigation or task planning. Some of the applications include automated wheelchairs, manufacturing tasks with robots retrieving parts from moving conveyors, air and freeway traffic control, etc. The motion of the obstacles can be unpredictable and new obstacles may be introduced in the environment. As a result, we need to develop appropriate algorithms for planning and executing appropriate trajectories in such dynamic scenes.There is extensive work on motion planning. Some of the widely used techniques are based on sample-based planning, though they are mostly limited to static environments. There is recent work on extending sample-based planning tech niques to dynamic scenes by incorporating the notion of time as an additional dimension in the configuration space [1], [2], [3]. However, the resulting algorithms may not generate smooth paths or handle dynamic constraints in real time. Other techniques for dynamic environments are either limited to local collision avoidance with the obstacles, or make some assumptions about the motion of dynamic obstacles.In this paper, we address the problem of collision-free tra jectory computation in dynamic scenes. In order to deal with unpredictable environments, we use replanning algorithms that interleave planning with execution [2], [4], [5], [6]. In these cases, the robot may only compute partial or sub optimal plans in the given time interval. In order to generate smooth paths and handle dynamic constraints, we combine replanning techniques with optimization-based planning [7], [8], [9].We present a novel parallel optimization-based motion planning algorithm for dynamic scenes. Our planning algo rithm optimizes multiple trajectories in parallel to explore Chonhyon Park, Jia Pan and Dinesh Manocha are with the . The accompanying video can be found at http://gamma.cs.unc.eduIITOMP. a broader subset of the configuration space and computes a high-quality path. The parallelization improves the optimality of the solution and makes it possible to compute a safe solution for the robot in a shorter time interval. We map our multiple trajectory optimization algorithm to many-core GPUs (graphics processing units) and utilize their massively parallel capabilities to achieve 20-30X speedup over serial optimizatio...