A biologically inspired meta-control navigation system for the Psikharpax rat robot

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e. g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics

A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles

Dynamic path planning is one of the key procedures for unmanned aerial vehicles (UAV) to successfully fulfill the diversified missions. In this paper, we propose a new algorithm for path planning based on ant colony optimization (ACO) and artificial potential field. In the proposed algorithm, both dynamic threats and static obstacles are taken into account to generate an artificial field representing the environment for collision free path planning. To enhance the path searching efficiency, a coordinate transformation is applied to move the origin of the map to the starting point of the path and in line with the source-destination direction. Cost functions are established to represent the dynamically changing threats, and the cost value is considered as a scalar value of mobile threats which are vectors actually. In the process of searching for an optimal moving direction for UAV, the cost values of path, mobile threats, and total cost are optimized using ant optimization algorithm. The experimental results demonstrated the performance of the new proposed algorithm, which showed that a smoother planning path with the lowest cost for UAVs can be obtained through our algorithm. (PDF) A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles. Available from: https://www.researchgate.net/publication/328765418_A_New_Dynamic_Path_Planning_Approach_for_Unmanned_Aerial_Vehicles [accessed Nov 20 2018]

Complete coverage path planning in an agricultural environment

The problem of finding a collision-free path through a region has garnered a lot of research over the years. One branch of this is the problem of finding a path that completely covers a region. Solutions to the complete coverage path planning problem have applications in many different areas, such as search and rescue, automotive painting, and agriculture. In many cases, it is not sufficient to find any route that completely covers the field. It is desired that the path also be optimal so as to minimize certain costs. This is especially true in the agricultural environment. In the area of precision farming alone, the complete coverage path planning problem exists while performing many different operations, such as harvesting, seeding, spraying, applying fertilizer, and tillage. The fundamental concern of farmers is reducing the costs of running the farm. Since most farming costs ultimately depend on time in the field and area covered, the more efficient an operation can be completed, the lower the costs. Optimality is thus typically in terms of finding the shortest complete coverage path through the field. In this paper, we present an O(n2) algorithm for solving the optimal complete coverage problem on a field boundary with n sides. This multi-phase algorithm makes use of a plane-sweep algorithm to subdivide the field into smaller, trapezoidal regions. The optimal paths through the subregions are then calculated. Finally there is a merge phase where it is determined whether neighboring regions can be more efficiently covered if they were merged together than if they were left separate

Learning Dynamic Robot-to-Human Object Handover from Human Feedback

Object handover is a basic, but essential capability for robots interacting with humans in many applications, e.g., caring for the elderly and assisting workers in manufacturing workshops. It appears deceptively simple, as humans perform object handover almost flawlessly. The success of humans, however, belies the complexity of object handover as collaborative physical interaction between two agents with limited communication. This paper presents a learning algorithm for dynamic object handover, for example, when a robot hands over water bottles to marathon runners passing by the water station. We formulate the problem as contextual policy search, in which the robot learns object handover by interacting with the human. A key challenge here is to learn the latent reward of the handover task under noisy human feedback. Preliminary experiments show that the robot learns to hand over a water bottle naturally and that it adapts to the dynamics of human motion. One challenge for the future is to combine the model-free learning algorithm with a model-based planning approach and enable the robot to adapt over human preferences and object characteristics, such as shape, weight, and surface texture.Comment: Appears in the Proceedings of the International Symposium on Robotics Research (ISRR) 201