Improved APF strategies for dual-arm local motion planning

Abstract

Manipulator motion planning is a classic problem in robotics, with a number of complete solutions available for their motion in controlled (industrial) environments. Due to recent technological advances in the field of robotics, there has been a significant development of more complex robots with high fidelity sensors and more computational power. One such example has been a rise in the production of humanoid robots equipped with dual-arm manipulators which require complex motion planning algorithms. Also, the technological advances have resulted in a shift from using manipulators in strictly controlled environments, to investigating the deployment of manipulators in dynamic or unknown environments. As a result, a greater emphasis has been put on the development of local motion planners, which can provide real-time solutions to these problems. Artificial Potential Fields (APF) is one such popular local motion planning technique, which can be applied to manipulator motion planning, however, the basic algorithm is severely prone to local minima problems. Here, two modified APF-based strategies for solving the dual-arm motion planning task in unknown environments are proposed. Both techniques make use of configuration sampling and subgoal selection to assist the APF in avoiding these local minima scenarios.Extensive simulation results are presented to validate the efficacy of the proposed methodology.

Autonomous Dual-Arm Manipulation of Familiar Objects

“…Even though the success of the search depends on the heuristic, the algorithm showed good performance in comparison with several samplingbased planners. Byrne et al [16] proposes a method consisting of goal configuration sampling, subgoal selection and Artificial Potential Fields (APF) motion planning. It was shown that the method improves APF performance for independent and cooperative dual-arm manipulation tasks.…”

A dual-arm manipulation strategy using position/force errors and Kalman filter

“…The motor in each joint provided more torque than the exact value to hold the object (Lee et al, 2014a). Byrne et al (2015) proposed strategies for dual-arm local motion planning in unknown environments. They were based on modified artificial potential fields, which use configuration sampling and sub-goal selection to avoid the local minimum.…”

Context-aware robotic arm using fast embedded model predictive control

“…For safety, speed and reliability it is imperative that collisions are avoided at all cost. Well established techniques of path planning and collision avoidance include artificial potential fields [5] and graph based search methods, such as rapidly-exploring random trees (RRT) [6]. These methods are effective but do not always compute the most efficient paths and incorporating dynamic constraints can be challenging.…”