Optimal arrest and guidance of a moving prismatic object using multiagents

Abstract

SUMMARYGenetic algorithm is used to determine the optimal capture points for the multi agents required to grasp a moving generic prismatic object by arresting it in form closure. Thereafter, the agents approach their respective moving goals using a decentralized projective path planning algorithm. Post arrest, the object is guided along a desired linear path to a desired goal point. Form closure of the object is obtained using the concept of accessibility angle. A convex envelop is formed around the object, and the goal points on the object boundary are mapped onto the envelope. The robots approach the mapped goal points first, and then, converge on the actual object. This ensures that the agents reach the actual goal points almost simultaneously, and do not undergo looping at a local concave region. The object is assumed alive while being captured but is assumed compromised thereafter. Post arrest, robots alter their positions optimally around the object to transport it along a desired direction. Frictionless point contact between the object and a robot is assumed. The shape of the mobile robot is considered cylindrical such that it can only apply force along the outward radial direction. Simulation results are presented that illustrate the effectiveness of the proposed method.

Experiments on multi-agent capture of a stochastically moving object using modified projective path planning

“…In ref. [29], which verified the planning algorithm in simulation. The projective path-planning algorithm herein is modified significantly compared to that in ref.…”

“…[29] and the work by Sudsang et al 6 In this work, prismatic objects and obstacles are all regarded to be moving stochastically (i.e., in an unplanned manner) and with low speeds within the 2D workspace. [29] and the work by Sudsang et al 6 In this work, prismatic objects and obstacles are all regarded to be moving stochastically (i.e., in an unplanned manner) and with low speeds within the 2D workspace.…”

“…[29] and the work by Sudsang et al 6 In this work, prismatic objects and obstacles are all regarded to be moving stochastically (i.e., in an unplanned manner) and with low speeds within the 2D workspace. [29] to facilitate hardware implementation and experimental validation in real-time. [29], the object moved along a known trajectory, while the obstacles were assumed stationary.…”

“…[29], the object moved along a known trajectory, while the obstacles were assumed stationary. [29]. [29] to facilitate hardware implementation and experimental validation in real-time.…”

Obstacle avoidance, arrest and guidance of a prismatic 2D object using multi agents

“…The robots use projective path planning as proposed in Sharma et al [16,17] to plan the paths avoiding the obstacles, other robots and the object. Robot movement towards the respective goal point on the c-space is divided into two steps, consisting of a translation and a rotation.…”