A Versatile Approach for Adaptive Grid Mapping and Grid Flex-Graph Exploration with a Field-Programmable Gate Array-Based Robot Using Hardware Schemes.

Robotic exploration in dynamic and complex environments requires advanced adaptive mapping strategies to ensure accurate representation of the environments. This paper introduces an innovative grid flex-graph exploration (GFGE) algorithm designed for single-robot mapping. This hardware-scheme-based algorithm leverages a combination of quad-grid and graph structures to enhance the efficiency of both local and global mapping implemented on a field-programmable gate array (FPGA). This novel research work involved using sensor fusion to analyze a robot's behavior and flexibility in the presence of static and dynamic objects. A behavior-based grid construction algorithm was proposed for the construction of a quad-grid that represents the occupancy of frontier cells. The selection of the next exploration target in a graph-like structure was proposed using partial reconfiguration-based frontier-graph exploration approaches. The complete exploration method handles the data when updating the local map to optimize the redundant exploration of previously explored nodes. Together, the exploration handles the quadtree-like structure efficiently under dynamic and uncertain conditions with a parallel processing architecture. Integrating several algorithms into indoor robotics was a complex process, and a Xilinx-based partial reconfiguration approach was used to prevent computing difficulties when running many algorithms simultaneously. These algorithms were developed, simulated, and synthesized using the Verilog hardware description language on Zynq SoC. Experiments were carried out utilizing a robot based on a field-programmable gate array (FPGA), and the resource utilization and power consumption of the device were analyzed.

Hardware Schemes for Smarter Indoor Robotics to Prevent the Backing Crash Framework Using Field Programmable Gate Array-Based Multi-Robots.

The use of smart indoor robotics services is gradually increasing in real-time scenarios. This paper presents a versatile approach to multi-robot backing crash prevention in indoor environments, using hardware schemes to achieve greater competence. Here, sensor fusion was initially used to analyze the state of multi-robots and their orientation within a static or dynamic scenario. The proposed novel hardware scheme-based framework integrates both static and dynamic scenarios for the execution of backing crash prevention. A round-robin (RR) scheduling algorithm was composed for the static scenario. Dynamic backing crash prevention was deployed by embedding a first come, first served (FCFS) scheduling algorithm. The behavioral control mechanism of the distributed multi-robots was integrated with FCFS and adaptive cruise control (ACC) scheduling algorithms. The integration of multiple algorithms is a challenging task for smarter indoor robotics, and the Xilinx-based partial reconfiguration method was deployed to avoid computational issues with multiple algorithms during the run-time. These methods were coded with Verilog HDL and validated using an FPGA (Zynq)-based multi-robot system.

A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot.

Service robots perform versatile functions in indoor environments. This study focuses on obstacle avoidance using flock-type indoor-based multi-robots. Each robot was developed with rendezvous behavior and distributed intelligence to perform obstacle avoidance. The hardware scheme-based obstacle-avoidance algorithm was developed using a bio-inspired flock approach, which was developed with three stages. Initially, the algorithm estimates polygonal obstacles and their orientations. The second stage involves performing avoidance at different orientations of obstacles using a heuristic based Bug2 algorithm. The final stage involves performing a flock rendezvous with distributed approaches and linear movements using a behavioral control mechanism. VLSI architectures were developed for multi-robot obstacle avoidance algorithms and were coded using Verilog HDL. The novel design of this article integrates the multi-robot's obstacle approaches with behavioral control and hardware scheme-based partial reconfiguration (PR) flow. The experiments were validated using FPGA-based multi-robots.