Stability-constrained mobile manipulation planning on rough terrain

Abstract

This paper presents a hierarchical framework that allows online point-to-point dynamic-stability-constrained optimal trajectory planning of a mobile manipulator robot working on rough terrain. First, the kinematics model of a mobile manipulator robot and the zero moment point stability measure are presented as theoretical background. Then, a sampling-based quasi-static planning algorithm modified for stability guarantee and traction optimization in continuous dynamic motion is presented along with a mathematical proof. The robot’s quasi-static path is then used as an initial guess to warm start a nonlinear optimal control solver which may otherwise have difficulties finding a solution to the stability-constrained formulation efficiently. The performance and computational efficiency of the framework are demonstrated through an application to a simulated timber harvesting mobile manipulator machine working on varying terrain. The results demonstrate feasibility of online trajectory planning on varying terrain while satisfying the dynamic stability constraint. Qualitative and quantitative comparisons with existing methods are also presented.

Modeling, analysis and control of robot–object nonsmooth underactuated Lagrangian systems: A tutorial overview and perspectives

“…Future robotics is likely to require highly complex tasks: bipedal machines which walk/run/jump/crawl and more [134,135,136,119], quadruped robots which walk/run/gallop/trot/amble/bound/pace/half bound/transversally gallop/kick/jump on their rear legs, robots which grasp/dexterously manipulate/push/tap/juggle [137,138,9], robots which throw/catch [139,140], which climb/slide along poles, quadropters which juggle/catch/throw a ball [141,142], manipulation with mobile manipulator for timber harvesting tasks [143], etc. In all cases the contact points should be allowed to stick or/and to slip [144], which is unavoidable as it is shown on simple examples in [118].…”

“…It may mean that in addition the robot's gravity center tracks some desired trajectory (position and velocity and acceleration), or that it reaches some apex height at each step. Specific stability measures have been developed to cope with this, the most well-known one in biped locomotion being the ZMP measure [25,101], also used in the context of manipulation [143]. More generic analyses on stability of complementarity Lagrangian systems [267] have been applied to biped robots [56].…”

“…Data-driven (empirical, machine learning, AI techniques, big data) approaches: from a general viewpoint, it is known that Automatic Control with "classical" modeling and Machine-Learning are complementary one each other [389,390,9]. Data-driven methods have been applied in manipulation and grasping [338,75,161,391,361,143], biped walking machines [392,393,394], hoppers [394],…”

Chance-Constrained Rollover-Free Manipulation Planning with Uncertain Payload Mass

“…Thus, the vehicle is dynamically stable when p zmp ∈ Conv(S), but has the tendency to roll over otherwise. More details on (1), (2), and (3) can be found in [21] but are omitted here to allow for the main focus of this paper.…”

Chance-Constrained Planning for Dynamically Stable Motion of Reconfigurable Vehicles

“…To achieve short computation time for complex formulations, hierarchical planning frameworks that reduce the problem dimension by employing multiple planning stages have been demonstrated in [7] and [8]. To solve the complete motion planning problem, the hierarchical frameworks first employ a sampling-based path planning method, such as the Rapidly-exploring Random Tree (RRT) [9], for geometric path planning.…”