Autonomous negotiating systems are composed of logically (even geographically) separated software agents that control logical or physical resources that altruistically seek to perform useful work in a cooperative manner. These systems are multi-agent systems that consist of a population of autonomous agents collaborating to work for a common goal while simultaneously performing their individual tasks (i.e., computational resources are distributed amongst interconnected agents). With the increasing capabilities of the collaborative agents, the need for faster and more efficient methods of utilizing the distributed resources has also increased. This paper focuses on improving the performance of one such multi-agent system that deals with the path planning for autonomous robots. This is achieved by exploiting parallelism among processing resources embedded in the autonomous vehicles, using a distributed memory, message-passing execution model.