A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot
Abstract
:1. Introduction
- Hardware scheme-based algorithms for the identification of obstacle types and their orientation in the indoor environment.
- The behavioral control mechanism approach has been developed for switching between formation to deformation and vice versa to execute obstacle avoidance.
- The decentralization of multi-robots using a hardware scheme-based heuristic algorithm to perform obstacle avoidance with respect to the type of the obstacle and its orientation. Partial reconfiguration (PR) flow integration was used to achieve optimized resource utilization on run-time implementation.
2. Hardware-Based Algorithms
2.1. Hardware-Based Algorithm for Obstacle Identification and Orientation
Algorithm 1: Pseudo code for identification of obstacle and orientation |
1. Initialize sensory distance and reference distances 2. Case (Obstacle) 3. State_1: if ((SFT && SFM) > dmax_ϑ0 0)? forward: State_2; 4. State_2: if ((SFT && SFM) ≤ dmax_ϑ0 0)? State_3: State_5; 5. State_3: if ((SFT == SFM) ≥ (dmin_ϑ0 0))? Alg_2@ Case_1: State_4; 6. State_4: if ((SFT ≥ dmin_ϑ0 0) && (SFM < dmin_ϑ0 0))? Alg_2@ Case_1: Orientation; 7. State_5: if ((SFT ≥ dmax _ϑ0 0) && (SFM ≥ dmin_ϑ0 0))? Alg_2@ Case_1: Orientation; 8. end case 9. Case (Orientation): 10. State_11: if ({SFT, SFM} @ ϑ ± 15 0, ±30 0, ±45 0, ±60 0, ±75 0) ≥ dmin)? Alg_2@ Case_2: State_12; 11. State_12: if ((SFT ≥ dmin_ϑ ± 15 0, ±30 0, ±45 0, ±60 0, ±75 0) && (SFM < dmin_ϑ ± 15 0, ±30 0, ±45 0, ±60 0, ±75 0))? Alg_2@ Case_2: State_13; 12. State_13: if ((SFT ≥ dmax_ϑ ± 15 0, ±30 0, ±45 0, ±60 0, ±75 0) && (SFM ≥ dmin_ϑ ± 15 0, ±30 0, ±45 0, ±60 0, ±75 0)), Alg_2@ Case_2; 13. end case |
2.1.1. Hardware-Based Algorithm for Obstacle Avoidance of Indoor Polygonal Objects
Algorithm 2: Pseudo code for obstacle avoidance |
1. Initialize obstacle identification and orientation 2. Case_1 (Obstacle avoidance) 3. State_1: if ((SFT == SFM) ≥ (dmin_ϑ0 0))? State_2: Case_2. 4. State_2: turn ϑ90 0 FL -> L & FR -> R; Wall follow (odometer++), 5. turn @edge ϑ90 0, FL -> R & FR -> L. 6. State_3: if ((FL (SR)) && (FR (SL)) = dmin_ϑ0 0) 7. Wall follow in Parallel to object, Take turn @edge ϑ90 0 FL -> R & FR -> L, 8. else 9. Wall follow in Perpendicular to object, Take turn @edge ϑ90 0 FL -> R & FR -> L. 10. end 11. Forward (odometer - -), Take turn ϑ90 0 FL -> L & FR -> R, end case. 12. Case_2 (Obstacle avoidance _ Orientation): 13. State_11: if ((FL (SFT)) && (FR (SFT)) ≠ dmin_ϑ0 0)? State_12: Case_1. 14. State_12: turn w.r.t to object orient ϑx± 0, FL -> L & FR -> R; Wall follow (odometer++). 15. turn @edge ϑx± 0, FL -> R & FR -> L. 16. State_13: Repeat; State_3. 17. end case |
2.1.2. Formation and Deformation of Multi-Robot in Indoor Environment
2.2. Hardware Schemes
2.2.1. Hardware Schemes of Obstacle Identification and Orientation
2.2.2. Hardware Schemes of Obstacle Avoidance for Distributed Multi-Robots
3. Results
3.1. Resource Utilization
3.2. Experimental Results
- Experimental results of multi-robot obstacle identification and avoidance
- Experimental results of multi-robot identification of oriented objects and avoidance.
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Module | LUT | BRAM | DSP Slice |
---|---|---|---|
Obstacle Identification and Orientation | 3724 | 18 | 8 |
Obstacle Avoidance | 8416 | 28 | 30 |
Interfacing Modules (sensors, motors, communication, Xilinx IP cores) | 6852 | 24 | 36 |
Control Unit and PWDC Sensor Fusion | 4468 | 20 | 42 |
Partial Reconfiguration Module | 5586 | 12 | 14 |
Behavioral Control Module | 6628 | 16 | 12 |
Total | 35,674 | 134 | 142 |
Reference Papers | Sensory Approach | Algorithm | Hardware | Pros | Cons | |
---|---|---|---|---|---|---|
Method | Fusion | |||||
[26] | RGB-D camera | X | Multi-attribute decision making | CPU | Reinforcement learning (RL) | Limited to simulation |
[27] | Virtual force | X | Hybrid force/ position | CPU | Fuzzy adaptive controller | Limited to simulation |
[28] | LIDAR | X | Multi-robot collision avoidance | CPU | Leader–follower formation control | Higher power consumption |
[29] | - | X | Nonlinear model predictive control | CPU | Dynamic obstacle avoidance | Limited to simulation |
[30] | _ | X | Dynamic obstacle avoidance of differential-drive wheeled mobile robot | CPU | Skidding and slipping analysis in obstacle avoidance | Limited to simulation |
[11] | Ultrasonic sensor | X | Centralized obstacle avoidance | FPGA | Hardware schemes for centralized multi-mobile robot’s obstacle avoidance | Partial reconfiguration not part of the hardware design |
Proposed | Ultrasonic sensor | √ | Centralization at formation and distribution at deformation method for obstacle avoidance | FPGA | Partial reconfiguration-based hardware schemes are a novel approach | Velocity-based obstacle avoidance will be addressed in future |
Environment Scenario | Ultrasonic Sensor Data Fusion | Capture Sensory Data Fusion @ Positive Rate | Error Rate |
---|---|---|---|
A | # Obstacle identification at normal orientation | 98.2% | 1.8% |
$ Obstacle identification at normal orientation | 99.4% | 0.6% | |
B | # Linear formation at normal orientation | 94.6% | 5.4% |
$ Linear formation at normal orientation | 97.8% | 2.2% | |
C | # Obstacle identification at angular orientation | 78.8% | 21.2% |
$ Obstacle identification at angular orientation | 96.2% | 3.8% | |
Transmission C to D | # Obstacle avoidance at angular orientation | 79.4% | 20.6% |
$ Obstacle avoidance at angular orientation | 96.8% | 3.2% | |
D | # flock formation | 86.4% | 13.6% |
$ flock formation | 97.4% | 2.6% |
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Basha, M.; Siva Kumar, M.; Chinnaiah, M.C.; Lam, S.-K.; Srikanthan, T.; Janardhan, N.; Hari Krishna, D.; Dubey, S. A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot. Sensors 2023, 23, 9480. https://doi.org/10.3390/s23239480
Basha M, Siva Kumar M, Chinnaiah MC, Lam S-K, Srikanthan T, Janardhan N, Hari Krishna D, Dubey S. A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot. Sensors. 2023; 23(23):9480. https://doi.org/10.3390/s23239480
Chicago/Turabian StyleBasha, Mudasar, Munuswamy Siva Kumar, Mangali Chinna Chinnaiah, Siew-Kei Lam, Thambipillai Srikanthan, Narambhatla Janardhan, Dodde Hari Krishna, and Sanjay Dubey. 2023. "A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot" Sensors 23, no. 23: 9480. https://doi.org/10.3390/s23239480