Recurrent Neural Network Based MultiRobot Route Planning for SteepLand Harvesting Systems Terrains with steep inclines cannot be utilized for crop production as it is hazardous to operate conventional farm equipment for various cropping tasks. This research, which is a part of a larger effort to deploy a team of robots for seeding and other similar tasks, proposes an approach for route planning of a team of ground mobile robots (drones) for seed delivery as well as an unmanned aerial vehicle (UAV) to continually replenish the drones with seeds. The overall cost is formulated in terms of linearly constrained integer quadratic programming (LCICQ). Using a Lagrangian function, a recurrent neural network with primal and dual sets of neurons is proposed to converge to optimal solutions.

Cumulative Training and Transfer Learning for MultiRobots CollisionFree Navigation Problems Recently, the characteristics of robot autonomy, decentralized control, collective decisionmaking ability, high fault tolerance, etc. have significantly increased the applications of swarm robotics in targeted material delivery, precision farming, surveillance, defense and many other areas. In these multiagent systems, safe collision avoidance is one of the most fundamental and important problems. Difference approaches, especially reinforcement learning, have been applied to solve this problem. This paper introduces a new cumulative learning approach which comprises of application of transfer learning with distributed multiagent reinforcement learning techniques to solve collisionfree navigation for swarm robotics. In our method, throughout the learning processes from the least complexity scenario to the most complex one, multiple agents can improve the shared policy through parameter sharing, reward shaping and multiround multisteps learning. We have adapted two policy gradient algorithms (TRPO and PPO) as the core of our distributed multiagent reinforcement learning method. The performance has shown that our new methodology can help reduce the training time and generate a robust navigation plan that can easily be generalized to complex indoor scenarios.