Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors

Low cost robots, such as vacuum cleaners or lawn mowers, employ simplistic and often random navigation policies. Although a large number of sophisticated localization and planning approaches exist, they require additional sensors like LIDAR sensors, cameras or time of flight sensors. In this work, we propose a global localization method biologically inspired by simple insects, such as the ant Cataglyphis that is able to return from distant locations to its nest in the desert without any or with limited perceptual cues. Like in Cataglyphis, the underlying idea of our localization approach is to first compute a pose estimate from pro-prioceptual sensors only, using land navigation, and thereafter refine the estimate through a systematic search in a particle filter that integrates the rare visual feedback. In simulation experiments in multiple environments, we demonstrated that this bioinspired principle can be used to compute accurate pose estimates from binary visual cues only. Such intelligent localization strategies can improve the performance of any robot with limited sensing capabilities such as household robots or toys.Comment: Accepted to BIOSIGNALS 201

Simultaneous localization and mapping with limited sensing using Extended Kalman Filter and Hough transform

Problem robota da izradi kartu nepoznatog okruženja uz ispravljanje vlastitog položaja na temelju iste karte i podataka senzora naziva se problem simultanog lokaliziranja i kartiranja (mapiranja). Budući da je točnost i preciznost senzora od velike važnosti u rješavanju tog problema, većina predloženih sustava uključuje primjenu skupih laserskih senzora daljine te relativno novije i jeftinije RGB-D kamere. Laserski senzori daljine su preskupi za neke primjene, a RGB-D kamere imaju veliku snagu, CPU ili sve što je potrebno za obradu podataka direktno ili na PC-u. Za izradu jeftinog robota bolje je primijeniti senzore niske cijene (poput infracrvenih ili sonarnih). Cilj je ovoga rada izraditi kartu nepoznatog okruženja uz primjenu jeftinog robota, produljenog Kalman filtra i linearnih obilježja, kao što su zidovi i namještaj. Ovdje se također predlaže pristup zatvaranja petlje. Eksperimenti su provedeni u okruženju Webots simulacije.The problem of a robot to create a map of an unknown environment while correcting its own position based on the same map and sensor data is called Simultaneous Localization and Mapping problem. As the accuracy and precision of the sensors have an important role in this problem, most of the proposed systems include the usage of high cost laser range sensors, and relatively newer and cheaper RGB-D cameras. Laser range sensors are too expensive for some implementations, and RGB-D cameras bring high power, CPU or communication requirements to process data on-board or on a PC. In order to build a low-cost robot it is more appropriate to use low-cost sensors (like infrared and sonar). In this study it is aimed to create a map of an unknown environment using a low cost robot, Extended Kalman Filter and linear features like walls and furniture. A loop closing approach is also proposed here. Experiments are performed in Webots simulation environment

Autonomous mobile robot berbasis landmark menggunakan particle filter dan occupancy grid maps untuk navigasi, penentuan posisi dan pemetaan.

Implementasi SLAM umumnya menggunakan robot dengan sensor yang lengkap. Di penelitian ini menggunakan HBE Robocar dengan sensor terbatas untuk mengetahui keberhasilan unsur pembangun SLAM (navigation, localization dan mapping) dan solusi ideal SLAM. Ketiga unsur SLAM direalisasikan dalam simulasi dan real robot dengan sistem persepsi sensor yang berbeda. Untuk penentuan posisi diberikan peta yang diketahui dan pergerakan robot menggunakan landmark berupa bola. Robot melakukan tracking color terhadap bola untuk mendapatkan pose relative dan diproses menggunakan algoritma particle filter dengan variasi jumlah partikel. Untuk navigasi, robot menghitung jalur terdekat dari start ke goal menggunakan algoritma dynamic A*. ============================================================================================ SLAM implementation commonly using robot with multi sensor. In this research, HBE Robocar was used for understanding its SLAM Developing component (navigation, localization, and mapping) with sensor limitation and its ideal solution. All of the SLAM components were conducted in simulation and real condition of robot with different sensor perception. Localization was conducted with known map and robot movement based on landmark using the balls. Robot does color tracking on the balls to get relative pose and processed using particle filter algorithm with various of particle quantity. Navigation was conducted by calculating shortest distance from start to goal using Dynamic A