ROBOT. Smooth Obstacle-avoidance by 4-wheel Navigational Vehicle Using Non-360 Range Sensors

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1 ROBOT Vol.35, No.5 Sep., 2013 DOI /SP.J bug 360 Pioneer3-AT bug 360 TP249 A (2013) Smooth Obstacle-avoidance by 4-wheel Navigational Vehicle Using Non-360 Range Sensors TAN Zhibin ZHAO Zuoxi ZHAO Ruqi LI Jiaojiao YU Long (Key Laboratory of Key Technology on Agricultural Machine and Equipment of South China Agricultural University, College of Engineering, South China Agricultural University, Guangzhou , China) Abstract: An obstacle-avoidance navigation algorithm based on bug algorithm is proposed for the vehicle using non-360 range sensors and having minimum turning radius. Then the vehicle can bypass obstacle edge along a continuous and smooth path and complete the fixed-goal navigation task. This algorithm uses virtual antenna to intuitively represent passable state in the front. The tentacle algorithm is used to analyze turning state, and the minimum turning radius of the vehicle is changed according to the surrounding environment to correct the turning route in real time. The test result on a Pioneer 3-AT wheeled robot show that the robot using the proposed algorithm can reach the fixed goal in an unknown environment without collision, and the trajectory is continuous and smooth. The algorithm solve the problem that bug algorithm cannot be used in this kind of vehicles. Keywords: smooth trajectory; non-360 range; obstacle avoidance; virtual antenna; tentacle 1 Introduction [1] [2] bug [3] [4-10] 360 bug bug bug [11] bug 45 [12] G32 zhao zuoxi@hotmail.com / /

2 bug [13] bug bug bug bug Pioneer3-AT 2 Design of virtual antennas [13] 1 D r L r W r O R max D recb L W b D recs L W s W b > W s D recs W s = W r W s W r D recb D recs W b W s L 3 Turning route selection mechanism bug von Hundelshausen [14] [15] [16] D recs R max D recb W s W b L O L r 2 W r Fig.2 Vehicle and tentacles distribution 1 Fig.1 Design of virtual antennas [14]

3 k r k ρ k R min, k = 0,,(n 3)/2 r k =, k = (n 1)/2 (1) ρ k 7 R min, k = (n + 1)/2,,n 1 R min ρ ρ ρ [14] ρ ρ ρ n k 0,1,,n 1 n (n 1)/2 (n 1)/2 (n 1)/2 R min 1/4 n = 13 ρ = 1.3 R min = 500 mm DIN O O 1 O θ O 1 O Y x k = r k cosθ L arc = r k θ πr (2) y k = r k sinθ + r k 0 2 L arc θ α k O O θ Y O 3 Fig.3 Simulated body position on tentacle diagram X 2 3 α k = θ P k = [x k,y k,α k ] T 3.3 P kt = [x kt,y kt,α kt ] T k t 1) 7 0,,6 4 H A T kt k H t I 0 B A a H 6 A C A t b (a) (b) (c) Fig.4 4 H Cases of the vehicle moving along different tentacles 2) 0 T 00 T 0a 7(a) T 0a D recb T 0a D recb B D r T 0a I[0] T 0a 3) 2) 1 T 10 T 1t 7(b) D recb D r I[1] T 1t H

4 ) 2 T 20 T 2t 4(c) T 2b D r T 2b D r C I[2] 5) I I 6) Implementation principle of the improved algorithm S T X O d(x,t ) X T d(x,o) X O H L L 4.1 bug bug bug bug bug T D scb D scs D scs Fig.5 5 R ng D scb D scs Bar Bar r R max bug Design of the original bug algorithm s virtual antennas x /mm Fig y /mm bug Experimental result of the original bug algorithm D scb D scs T 4.2 bug bug bug i) T ii) S T T 3 1 Tab.1 1 Differences between the new and original algorithms 0 3 5

5 T D recs i) T ii) D recs H ) i) D recs ii) g m g g = d(x,o)/2 (3) 2) S (1) S T T T φ T i) T ii) D recs H (2) H L i) ii) D recs iii) D recs i) D recb ii) T (3) i) g ii) D recs i) D recs ii) T bug D recs T i) D recs ii) D recs d(x,t ) < d(x,o) S T D recs I H D D recb J (6) E T F L T SHDEFLT SH,DE,LT HD EF,FL

6 S LMS200 H MobileRobots C++ D E ARIA C++ ARIA I J F L T 7 Fig.7 Trajectory simulation of bypassing obstacle T S 8 Fig.8 Unreachable goal situation Test on a mobile robot and its result analysis Pioneer3-AT Pioneer3-AT MobileRobots 9 Pioneer3-AT Fig.9 Pioneer3-AT mobile robot platform [17] 10 m 7 m 10 L r =580 mm W r =493 mm R min =500 mm R max = 5000 mm L = 700 mm W b = 780 mm W s = 600 mm n = 13 ρ = 1.3 Matlab 10 Fig.10 Test environment

7 bug T S T (0,0) (8000,0) (H 1 H 2 ) (L 1 L 2 ) bug (H 1H 2 ) (L 1L 2 ) bug T Tab.1 2 Comparison of obstacle-avoidance results x /mm L 2 H 2 L 1 /mm (7934, 28) (7960,20) /mm (8300, 50) (8380,100) /mm Fig.11 x /mm Fig H 1 S 0 y /mm 5000 Test result of goal navigation with obstacle avoidance using the proposed algorithm L 2 H 1 T S H 2 L 1 0 y /mm bug 5000 Test result of goal navigation with obstacle avoidance using the broken-line bug algorithm H 1 L 2 6 Conclusion (1) bug 180 (2) (3) [17] References [1] [J] (2) Wang R B, Li B, Chu J W, et al. A review of the safety guarantee technology of worldwide intelligent vehicle[j]. Journal of Highway and Transportation Research and Development, 2002, 19(2): [2] [J] (4) Meng J H, Zhu J H, Sun Z Q. A new path planning algorithm for sensor-based mobile robot in unknown environment[j]. Robot, 2005, 27(4): [3] Lumelsky V J, Stepanov A A. Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape[j]. Algorithmica (New York), 1987, 2(4): [4] Sankaranarayanan A, Vidyasagar M. A new path planning algorithm for moving a point object amidst unknown obstacles in a plane[c]//ieee International Conference on Robotics and Automation. Piscataway, USA: IEEE, 1990:

8 [5] Sankaranarayanan A, Vidyasagar M. Path planning for moving a point object admidst unknown obstacles in a plane: A new algorithm and a general theory for algorithm development[c]// 29th IEEE Conference on Decision and Control. Piscataway, USA: IEEE, 1990: [6] Kamon I, Rivlin E. Sensory-based motion planning with global proofs[j]. IEEE Transactions on Robotics and Automation, 1997, 13(6): [7] Lumelsky V J, Skewis T. Incorporating range sensing in the robot navigation function[j]. IEEE Transactions on Systems, Man, and Cybernetics, 1990, 20(5): [8] Kamon I, Rimon E, Rivlin E. TangentBug: A range-sensorbased navigation algorithm[j]. International Journal of Robotics Research, 1998, 17(9): [9] Magid E, Rivlin E. CautiousBug: A competitive algorithm for sensory-based robot navigation[c]//ieee/rsj International Conference on Intelligent Robots and Systems. Piscataway, USA: IEEE, 2004: [10] Choset H, Lynch K M, Hutchinson S, et al. Principles of robot motion: Theory, algorithms, and implementations[m]. Cambridge, UK: A Bradford Book, [11] [J] (4) Liu X, Chen J X. A limited field-of-view based obstacle avoidance and path planning algorithm for mobile robots[j]. Aerospace Control and Application, 2008, 34(4): [12] [J] (5) Hao Z B, Hong B R. Sensor-based path planning for mobile robot in unknown environment[j]. Acta Electronica Sinica, 2006, 34(5): [13] 360 [J] (19) Zhao Z X, Wang N, Zhan Z G, et al. Implementation of sensorbased navigation on mobile robots using non-360 range sensor[j]. Journal of Mechanical Engineering, 2010, 46(19): [14] von Hundelshausen F, Himmelsbach M, Hecker F, et al. Driving with tentacles: Integral structures for sensing and motion[j]. Journal of Field Robotics, 2008, 25(9): [15] [J] (4) Wang K K, Zhao H Q, Lu Q, et.al. A motion planning method for autonomous land vehicles based on virtual tentacles[j]. Journal of Academy of Armored Force Engineering, 2011, 25(4): [16] [J] (6) Niu R X, Xia J T, Wang X H, et al. Tentacle algorithm of obstacle avoidance and autonomous driving for intelligent vehicle[j]. Journal of Traffic and Transportation Engineering, 2010, 10(6): [17] [M] Yu Z S. The theory of automobile[m]. 3rd ed. Beijing: China Machine Press,

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