Carlos Mastalli

Planning and execution of dynamic whole-body locomotion for a hydraulic quadruped on challenging terrain

We present a framework for dynamic quadrupedal locomotion over challenging terrain, where the choice of appropriate footholds is crucial for the success of the behaviour. We build a model of the environment on-line and on-board using an efficient occupancy grid representation. We use Any-time-Repairing A* (ARA*) to search over a tree of possible actions, choose a rough body path and select the locally-best footholds accordingly. We run a n-step lookahead optimization of the body trajectory using a dynamic stability metric, the Zero Moment Point (ZMP), that generates natural dynamic whole-body motions. A combination of floating-base inverse dynamics and virtual model control accurately executes the desired motions on an actively compliant system. Experimental trials show that this framework allows us to traverse terrains at nearly 6 times the speed of our previous work, evaluated over the same set of trials.

Hierarchical planning of dynamic movements without scheduled contact sequences

Most animal and human locomotion behaviors for solving complex tasks involve dynamic motions and rich contact interaction. In fact, complex maneuvers need to consider dynamic movement and contact events at the same time. We present a hierarchical trajectory optimization approach for planning dynamic movements with unscheduled contact sequences. We compute whole-body motions that achieve goals that cannot be reached in a kinematic fashion. First, we find a feasible CoM motion according to the centroidal dynamics of the robot. Then, we refine the solution by applying the robots full-dynamics model, where the feasible CoM trajectory is used as a warm-start point. To accomplish the unscheduled contact behavior, we use complementarity constraints to describe the contact model, i.e. environment geometry and non-sliding active contacts. Both optimization phases are posed as Mathematical Program with Complementarity Constraints (MPCC). Experimental trials demonstrate the performance of our planning approach in a set of challenging tasks.

On-line and on-board planning and perception for quadrupedal locomotion

We present a legged motion planning approach for quadrupedal locomotion over challenging terrain. We decompose the problem into body action planning and footstep planning. We use a lattice representation together with a set of defined body movement primitives for computing a body action plan. The lattice representation allows us to plan versatile movements that ensure feasibility for every possible plan. To this end, we propose a set of rules that define the footstep search regions and footstep sequence given a body action. We use Anytime Repairing A* (ARA*) search that guarantees bounded suboptimal plans. Our main contribution is a planning approach that generates on-line versatile movements. Experimental trials demonstrate the performance of our planning approach in a set of challenging terrain conditions. The terrain information and plans are computed on-line and on-board.

Trajectory and foothold optimization using low-dimensional models for rough terrain locomotion

We present a trajectory optimization framework for legged locomotion on rough terrain. We jointly optimize the center of mass motion and the foothold locations, while considering terrain conditions. We use a terrain costmap to quantify the desirability of a foothold location. We increase the gaits adaptability to the terrain by optimizing the step phase duration and modulating the trunk attitude, resulting in motions with guaranteed stability. We show that the combination of parametric models, stochastic-based exploration and receding horizon planning allows us to handle the many local minima associated with different terrain conditions and walking patterns. This combination delivers robust motion plans without the need for warm-starting. Moreover, we use soft-constraints to allow for increased flexibility when searching in the cost landscape of our problem. We showcase the performance of our trajectory optimization framework on multiple terrain conditions and validate our method in realistic simulation scenarios and experimental trials on a hydraulic, torque controlled quadruped robot.

Simultaneous Contact, Gait and Motion Planning for Robust Multi-Legged Locomotion via Mixed-Integer Convex Optimization

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 robots 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.

Bio-inspired holistic control through modular relative Jacobian for combined four-arm robots

Biological limbs normally come in pairs: mammals have four, insects have six, arachnids have eight, and centipedes have one pair of legs per body segment. This work attempts to interpret the biological method of controlling paired legs (here treated as dual-arms) in opposite and adjacent pairs to achieve a holistic controller of a large four-legged animal (here treated as a combined four-arm robot). A modular relative Jacobian controls a dual-arm as a single manipulator with a single end-effector, and is expressed in terms of the Jacobians of each of the stand-alone manipulators. In this work, the two opposite pairs of legs are treated as single end-effector dual-arms, and then these two dual-arms are combined together to form a single end-effector four-arm robot. The four-arm controller uses the same principle as a single end-effector controller of a dual-arm, and thus results into a single end-effector controller of a four-arm. A modular relative Jacobian of the four arms is derived. Gazebo simulation results are shown for two gait patterns of a four-legged animal, namely, pacing and trotting.

A Proposed Architecture for Autonomous Operations in Backhoe Machines

In this work is developed an architecture which consists of four main components: perception system, tasks planning, motion planner, and control systems that allow autonomous operations in backhoe machines. In the first part is described the architecture of control system. A set of techniques for collision mapping of the scene is described and implemented. Moreover, a motion planning system based on Learning from Demonstration using Dynamic Movement Primitives as control policy is proposed, which allows backhoe machines to perform operations in autonomous manner. A statement of reasons is presented, wherein we justified the implementation of such motion system versus planners like \(\text {A}^*\), Probabilistic RoadMap (PRM), Rapidly-exploring Random Tree (RRT), etc. In addition, we present the performance of the architecture in a simulation environment.

Extracting points features from laser rangefinder data based on hough transform

This paper describes a novel feature extraction method for laser rangefinder data. Extracted features correspond to real and virtual corners of the scanned scene. The method is based on the Hough Transform (HT) for line extraction, where the intersecting points of these lines are considered as features. This work highlights the use of the HT outside of image applications, and presents a new filtering algorithm that reduces false positive in line detection by the HT based method. The developed method was tested under various simulated benchmarks in order to compare the performance as a function of correctness, uncertainty, execution time and other parameters. Also, a real data benchmark was included in the tests. Finally, a simulation of EKF-SLAM was performed to demonstrate the functionality of the developed method in more complex tasks.