Humanoid robots dynamically navigate an environment by interacting with it via contact wrenches exerted at intermittent contact poses. Therefore, it is important to consider dynamics when planning a contact sequence. Traditional contact planning approaches assume a quasi-static balance criterion to reduce the computational challenges of selecting a contact sequence over a rough terrain. This however limits the applicability of the approach when dynamic motions are required, such as when walking down a steep slope or crossing a wide gap. Recent methods overcome this limitation with the help of efficient mixed integer convex programming solvers capable of synthesizing dynamic contact sequences. Nevertheless, its exponential-time complexity limits its applicability to short time horizon contact sequences within small environments. In this paper, we go beyond current approaches by learning a prediction of the dynamic evolution of the robot centroidal momenta, which can then be used for quickly generating dynamically robust contact sequences for robots with arms and legs using a search-based contact planner. We demonstrate the efficiency and quality of the results of the proposed approach in a set of dynamically challenging scenarios.
Footstep planning for humanoid robot has been studied extensively @cite_15 @cite_35 @cite_12 @cite_30 @cite_24 @cite_7 @cite_21 . In these works, the planner plans a footstep sequence to avoid obstacles on the ground and remain inside the specified contact regions on a flat or piecewise-flat ground. To increase the likelihood of success, they incorporate an approximation of robot balance and kinematic reachability into the contact transition model, and do not explicitly perform balance check online. There are also works addressing contact planning in unstructured environment using both palm and foot contacts @cite_22 @cite_18 @cite_1 @cite_26 @cite_0 . However, these approaches assumes quasi-static motions, and drops solutions involving dynamic motions.
@cite_30 @cite_35 @cite_18 @cite_26 @cite_22 @cite_7 @cite_21 @cite_1 @cite_24 @cite_0 @cite_15 @cite_12
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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 subproblems: 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 in dynamic simulations or on the real HRP-2 robot. We propose a humanoid robot navigation planning framework that reuses previous experience to decrease planning time. The framework is intended for navigating complex unstructured environments using both palm and foot contacts. In a complex environment, discrete-search-based contact space planners trade-off between high branching factor and action flexibility. Although approaches such as weighted A∗, ARA∗ and ANA∗ could speed up the search by compromising on optimality, they can be very slow when the heuristic is inaccurate. In the proposed framework, an experience-retrieval module is added in parallel to ANA∗. This module collects previously-generated motion plans and clusters them based on contact pose similarity to form a motion plan library. To retrieve an appropriate plan from the library for a given environment, the framework uses a distance between the contact poses in the plan and environment surfaces. Candidate plans are then modified with local trajectory optimization until a plan fitting the query environment is found. Our experiments show that the proposed framework outperforms planning-from-scratch in success rate in unstructured environments by at least 28 and can navigate difficult environments such as rubble and narrow corridors. We present a new method for planning footstep placements for a robot walking on uneven terrain with obstacles, using a mixed-integer quadratically-constrained quadratic program (MIQCQP). Our approach is unique in that it handles obstacle avoidance, kinematic reachability, and rotation of footstep placements, which typically have required non-convex constraints, in a single mixed-integer optimization that can be efficiently solved to its global optimum. Reachability is enforced through a convex inner approximation of the reachable space for the robot's feet. Rotation of the footsteps is handled by a piecewise linear approximation of sine and cosine, designed to ensure that the approximation never overestimates the robot's reachability. Obstacle avoidance is ensured by decomposing the environment into convex regions of obstacle-free configuration space and assigning each footstep to one such safe region. We demonstrate this technique in simple 2D and 3D environments and with real environments sensed by a humanoid robot. We also discuss computational performance of the algorithm, which is currently capable of planning short sequences of a few steps in under one second or longer sequences of 10–30 footsteps in tens of seconds to minutes on common laptop computer hardware. Our implementation is available within the Drake MATLAB toolbox [1]. We present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains. Humanoid robots have abilities of stepping over or onto obstacles, which is different from wheeled robots. However it may be difficult to apply the ordinary motion planning methods such as Rapidly-exploring Random Trees (RRT) to humanoid robots directly. Because these kinds of methods only consider to circumvent obstacles and ignore the constraint of balance. Aiming at dealing with these problems in one frame, a novel approach based on hierarchical RRT is used to plan the footstep for humanoid robots. It is designed according to three basic constraints: a transition model based gait generator, an inverted pendulum based balance controller and a collision detection based path planner. First, a set of layered transition model is utilized to revise the footstep according to the terrain condition, which is able to take full use of the motion ability as well as improve the efficiency. Then, a hierarchical strategy is exploited to select the feasible foot location to be added in the random tree based on the results of collision checking and balance control. Finally, a dynamic RRT method is introduced in our work to revise paths in changing environments. Different experiments are given to verify the feasibility and performance of the proposed approach in complicated environments with both dynamic and static obstacles. This paper proposes a new control scheme of decentralized multi-legged robot based on Follow-the-Contact-Point (FCP) gait control. In this control scheme, the first legs contact the points allocated on the terrain and the following legs touch the foot on the point which the fore leg is contacting. By creating adequate contacting points on the environment, the robot can be navigated successfully. Since the position information of the contacting points is relayed based on physical information of the legs, each leg does not need the global position of the contacting point. As a result, the proposed control scheme realizes decentralized architecture. This paper introduces the control law for the robot walking on even terrain. Finally, the result of physical simulation of 20-legged robot shows the availability of the proposed method. This paper presents a generic and efficient approach to generate dynamically consistent motions for under-actuated systems like humanoid or quadruped robots. The main contribution is a walking pattern generator, able to compute a stable trajectory of the center of mass of the robot along with the angular momentum, for any given configuration of contacts (e.g. on uneven, sloppy or slippery terrain, or with closed-gripper). Unlike existing methods, our solver is fast enough to be applied as a model-predictive controller. We then integrate this pattern generator in a complete framework: an acyclic contact planner is first used to automatically compute the contact sequence from a 3D model of the environment and a desired final posture; a stable walking pattern is then computed by the proposed solver; a dynamically-stable whole-body trajectory is finally obtained using a second-order hierarchical inverse kinematics. The implementation of the whole pipeline is fast enough to plan a step while the previous one is executed. The interest of the method is demonstrated by real experiments on the HRP-2 robot, by performing long-step walking and climbing a staircase with handrail support. Traditional motion planning approaches for multilegged locomotion divide the problem into several stages, such as contact search and trajectory generation. However, reasoning about contacts and motions simultaneously is crucial for the generation of complex whole-body behaviors. Currently, coupling theses problems has required either the assumption of a fixed gait sequence and flat terrain condition, or nonconvex optimization with intractable computation time. In this letter, we propose a mixed-integer convex formulation to plan simultaneously contact locations, gait transitions, and motion, in a computationally efficient fashion. In contrast to previous works, our approach is not limited to flat terrain nor to a prespecified gait sequence. Instead, we incorporate the friction cone stability margin, approximate the robot's torque limits, and plan the gait using mixed-integer convex constraints. We experimentally validated our approach on the HyQ robot by traversing different challenging terrains, where nonconvexity and flat terrain assumptions might lead to suboptimal or unstable plans. Our method increases the motion robustness while keeping a low computation time. We present an algorithm for planning goal-directed footstep navigation strategies for biped robots through obstacle-filled environ- ments and uneven ground. Planning footsteps is more general than most existing navigation methods designed for wheeled robots, since the op- tions of stepping over or upon obstacles are available. Given a height map of the terrain and a discrete set of possible footstep motions, the planner uses an A* search to generate a sequence of footstep locations to reach a given goal state. The planner evaluates footstep locations for viability using a collection of heuristic metrics designed to encode the relative safety, effort required, and overall motion complexity. We show preliminary results of the planner over several simulated terrains, as well as a simplified, online version of the algorithm running on the H7 hu- manoid robot. In the latter case, a stereo vision system is used to sense obstacles in the immediate environment and identify a target goal loca- tion, which is used to update the current optimal footstep sequence to the goal from the robot's present location. We propose a method for checking and enforcing multicontact stability based on the zero-tilting moment point (ZMP). The key to our development is the generalization of ZMP support areas to take into account: 1) frictional constraints and 2) multiple noncoplanar contacts. We introduce and investigate two kinds of ZMP support areas. First, we characterize and provide a fast geometric construction for the support area generated by valid contact forces, with no other constraint on the robot motion. We call this set the full support area. Next, we consider the control of humanoid robots by using the linear pendulum mode (LPM). We observe that the constraints stemming from the LPM induce a shrinking of the support area, even for walking on horizontal floors. We propose an algorithm to compute the new area, which we call the pendular support area. We show that, in the LPM, having the ZMP in the pendular support area is a necessary and sufficient condition for contact stability. Based on these developments, we implement a whole-body controller and generate feasible multicontact motions where an HRP-4 humanoid locomotes in challenging multicontact scenarios. This paper addresses a new control strategy to reduce the damage to a humanoid robot during a fall. Instead of following the traditional approach of finding a favorable configuration with which to fall to the ground, this method attempts to stop the robot from falling all the way to the ground. This prevents the full transfer of the robot's potential energy to kinetic energy, and consequently results in a milder impact. The controlled motion of the falling robot involves a sequence of three deliberate contacts to the ground with the swing foot and two hands, in that order. In the final configuration the robot's center of mass (CoM) remains relatively high from the floor and the robot has a relatively stable three-point contact with the ground; hence the name tripod fall. The optimal location of the three contacts are learned through reinforcement learning algorithm. The controller is simulated on a full size humanoid, and experimentally tested on the NAO humanoid robot. In this work we apply our fall controller only to a forward fall. Despite the stable walking capabilities of modern biped humanoid robots, their ability to autonomously and safely navigate obstacle-filled, unpredictable environments has so far been limited. We present an approach to autonomous humanoid walking that combines vision-based sensing with a footstep planner, allowing the robot to navigate toward a desired goal position while avoiding obstacles. An environment map including the robot, goal, and obstacle locations is built in real-time from vision. The footstep planner then computes an optimal sequence of footstep locations within a time-limited planning horizon. Footstep plans are reused and only partially recomputed as the environment changes during the walking sequence. In our experiments, combining real-time vision with plan reuse has allowed a Honda ASIMO humanoid robot to autonomously traverse dynamic environments containing unpredictably moving obstacles