Autonomous movement generation for manipulators with multiple simultaneous constraints using the attractor dynamics approach

Abstract

The movement of autonomous agents in natural environments is restricted by potentially large numbers of constraints. To generate behavior that fulfills all given constraints simultaneously, the attractor dynamics approach to movement generation represents each constraint by a dynamical system with attractors or repellors at desired or undesired values of a relevant variable. These dynamical systems are transformed into vector fields over the control variables of a robotic agent that force the state of the whole system in directions beneficial to the satisfaction of the behavioral constraint. The attractor dynamics approach was recently successfully applied to the generation of manipulator motion trajectories avoiding collision with obstacles [1] and constraints on gripper orientation during reaching and grasping movements [2]. Continuing that body of work, this paper proposes a system which generates movements satisfying both obstacle avoidance and gripper orientation constraints simultaneously. As an extension, the additional constraint of avoiding hardware limits for joint angles is included. Properties of the resulting system are demonstrated by a systematic study generating movements with a large number of constraints in different scene setups. Specific characteristics are highlighted by several showcase example movements.

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System

“…In this framework, obstacle avoidance has been integrated with target acquisition and successful navigation in an unknown environment has been demonstrated both for vehicles and robotic arms (Reimann et al, 2011). This approach is similar to another successful reactive approach to obstacle avoidance: the potential field approach (e.g., Haddad et al, 1998), in which the target creates a global minimum in a potential that drives the robot, whereas obstacles create elevations in this potential.…”

Towards a scientific foundation for engineering Cognitive Systems – A European research agenda, its rationale and perspectives

“…This holds for instance for eSMCs ("Extending sensorimotor contingencies to cognition", http://esmcs.eu/) which, building on the concept of sensorimotor contingencies (O'Regan & Noe, 2001)), attempts to bridge the gap between low-level and high-level cognitive competencies (Maye & Engel, 2011). Another current project that stands out in this regard is NeuralDynamics ("A neuro-dynamic framework for cognitive robotics: scene representations, behavioural sequences, and learning", http://cordis.europa.eu/ projects/97477_en.html) which applies the conceptual apparatus of Dynamic Field Theory to endowing robots with high-level faculties (Reimann et al, 2011).…”

A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating

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