Networking of Multi-Robot Systems: Architectures and Requirements
- Multiple robots can concurrently work on the task to achieve it faster.
- Robots can be heterogenous in their capabilities to provide a cost-effective solution to achieve a task where each robot handles specific components of the task matching its capabilities.
- Multiple robots can effectively deal with a task that is inherently distributed over a wide area.
- Using multiple robots for achieving a task provides fault tolerance as the presence of multiple robots capable of similar processes can be used to compensate when any of them fails.
2. Types of MRS Systems and Their Applications
2.1. Types of MRS Systems
2.1.1. Collective Swarm Systems
2.1.2. Intentionally Cooperative Systems
- Strongly cooperative: In this class of MRS systems, the robots act cooperatively to achieve a common goal. Consequently, this kind of close coordination require appropriate communication and synchronization which typically has more stringent quality of service (QoS) requirements such as bandwidth, and delay.
- Weakly cooperative: In this class of MRS systems, the robots divide and coordinate the tasks that need to be done among themselves. Afterwards, each robot proceeds to achieve its task with a form of operational independence. In this case, the supporting communication protocols and corresponding QoS requirements are more relaxed.
2.2. MRS Applications
3. MRS Architectures
- Centralized: In this category a single point of control manages the behavior of all the robots in the team . Such architecture suffers from the single point of failure problem, which can reduce its reliability. Also, the scalability is diminished. This is because the central controller must be constantly aware of the state of the all the team members which triggers the exchange of numerous messages in addition to the control messages which must also be sent back to the individual robots to control their actions.
- Hierarchical: In this category, the robots are organized in a command and control hierarchy similar to that in the military. Specifically, in this strategy, a robot controls a group of other robots. Each of those robots in turn controls a group of other robots. This pattern can continue for several levels down the hierarchy depending on the size of the network. This approach, it is highly scalable and can be appropriate for some applications with a large number of robots. However, it has reduced reliability due to the considerable vulnerability in handling failures of robots at the higher levels in the hierarchy.
- Decentralized: This is the most common category for MRS systems. In this case, robots take actions based on their own local view following certain strategic guidelines and goals for the team. This model is characterized by its robustness and ability to adjust to failures, since no centralized control is used. On the other hand, it is a challenge to keep the synchronization and coherency among the robots. In addition, it is not trivial to coordinate actions when mission objectives change.
- Hybrid: This approach combines a local decentralized control, which provides robustness with hierarchical control to achieve global synchronization and coordination of actions, goals, and tasks. This hybrid strategy is used in many MRS systems, which need to have scalability due to the large size of the network as well as an ability to take quick decisions on the local level to achieve better performance and quicker reaction to local events and failures.
4. Communication Issues and Requirements for MRS Systems
4.1. MANETs and MRS
- Static routing: In this class of protocols, the route is pre-programmed in to the routing tables of the individual nodes before the mission. Although this can be useful for some missions with relatively fixed and predetermined topology, it is inflexible and very limiting for MRS systems, which are characterized by higher mobility and variable topology.
- Proactive routing: In this class of protocols, a routing table is constructed and periodically maintained by the nodes even before transmission requests arrive from the upper layers to the network layer. This strategy increases the overhead of route discovery and maintenance messages which reduces efficiency in cases where the node-to-node traffic is limited and decreases the scalability of the corresponding MRS network. However, it can be suitable in cases where the application cannot tolerate large and unpredictable route discovery delays such as MRS networks with high real-time coordination involving most of the nodes. The destination-sequenced distance-vector (DSDV) protocol is an example of such protocols.
- Reactive routing: In this case, an end-to-end path between the source and the destination is only discovered on-demand when it is needed. Once the route is discovered the routing table at the source node is updated with the corresponding entry. All subsequent transmissions from that node to the destination use the discovered path as long as the related topology and relative locations of the intermediate nodes are not changed in a way that leads to the breaking of one or more intermediate links. If the path is no longer valid, a new path discovery process is initiated. This routing strategy reduces the control message overhead, since the routes are only discovered when they are needed. It is appropriate for MRS networks with a relatively larger number of nodes and lower traffic which is limited to the occurrence of certain events with a selected number of nodes involved such as search and rescue, and environmental monitoring applications. Dynamic source routing (DSR), ad hoc on-demand distance vector (AODV), and temporally ordered routing algorithm (TORA) are examples of such protocols.
- Hybrid routing: In this case, both proactive and reactive routing strategies are used in the same MRS network. This type of routing aims at benefiting from the advantages of both strategies by applying them under different conditions. For example, the network can be divided into multiple geographic clusters with selected cluster head (CH) nodes in each cluster. Consequently, intra-cluster traffic, which consists of data exchanges among nodes in the same cluster uses a proactive routing strategy since the nodes are likely to have more frequent exchanges due to closer cooperation and tighter coordination. The inter-cluster traffic, which consists of data exchanges among nodes in different clusters uses a reactive routing strategy, which routes the data among the CHs in different clusters. Traffic that is exchanged among nodes in different clusters is routed to the CH of the source cluster, which transmits the data to the CH in the destination cluster. The latter subsequently routes the data to the destination node in that cluster. This approach provides a scalability advantage and is appropriate for larger MRS networks with clusters of robots responsible for closely coordinated tasks in the same geographic area (or cluster) but need to communicate information to robots in other geographic areas (or clusters) less frequently.
- Location/Geographic routing: In this type of routing, each node must be equipped with GPS circuitry or must be provided with the position information of other nodes in the network from a location service. This comes at the expense of increased node cost. Data is routed from the source to the destination using various routing strategies which are intended to keep moving the data closer to the geographic location of the destination until the latter is reached. This type of routing has the advantage that each node does not need to keep track of the topology of the network. It only needs to determine the next hop that gets the data closer to the destination. The routing overhead is reduced since the routing decision is based on local information and the routing process typically incurs lower delays. This type of routing can be useful for MRS networks stretching over larger geographic areas and the nodes are highly mobile leading to constant topology changes. Protocols in this category include Location-Aided Routing in mobile ad hoc networks (LAR) , Energy Efficient Location-Aided Routing (EELAR) , greedy location-aided routing protocol (GLAR) , Location-Aided Energy Efficient Routing (LAEER) , and Location-Based Efficient Routing Protocol (ALERT) .
- Hierarchical protocols: In this case, a multi-level hierarchy is adopted. Consequently, communication between the lower level nodes is done by passing the data to the upper levels. For example, a three-level hierarchy can be used where the lower level robots play a sensing/acting role. They are identified as basic robots (BR). The robots at the second level play a data relay role, where the data between various clusters of lower level robots is relayed among the data relay robots (DRR). The robots at the third level are identified as data dissemination robots (DDR), and are responsible for delivering the data collected by the DRRs to the control center (CC). This type of strategy provides higher scalability at the cost increased complexity, and cost. However, for MRS networks with a large number of small robots (hundreds or thousands) covering a wide geographic area, this hierarchical model would be appropriate. Hierarchical routing protocols include Low-Energy Adaptive Clustering (LEACH), Power-Efficient GAthering in Sensor Information Systems (PEGASIS), and AdaPtive Threshold sensitive Energy Efficient sensor Network protocol (APTEEN) [37,38,39].
4.2. MRS Networking Protocol Issues and Requirements
- Number of robots: Some networking protocols operate efficiently only when the number of nodes in the network is small. Such protocols might not be practical for large networks. This can be the result of a high number of control message exchanges that are not localized to the event area for example.
- GPS capability: Depending on the routing protocol, some routing strategies might assume that each node knows its location as well as the location of its neighbors. This leads to an increase in node cost and can be detrimental for MRS systems where a large number or inexpensive robots is desirable.
- On-board processing: Some routing protocols require a large amount of processing, which might not be available in some or all the nodes in an MRS networks. For such networks, it is essential to choose a simple routing protocol that can make its routing decisions without complicated logic that takes too many parameters into consideration.
- On-board memory: The amount of memory required to store routing table information as well as other routing-related parameters varies widely between different routing protocols. For example, link-state protocols require the topology of the entire network to be stored by each node, which significantly increases the amount of memory needed for larger networks. This is not the case for distance-vector protocols. Consequently, for large MRS networks with small inexpensive robots, it is important to choose protocols, which do not require a lot of memory. This requirement is not so important for smaller networks with more capable larger robots with considerably larger memory resources.
- Energy: Robots come in various sizes and shapes. They range from tiny robots that are smaller than the human hand, or even microscopic to ones, to ones that are as big as a large-size UAV. However, in most cases, they draw their energy from a battery, which has a limited capacity. Consequently, the networking protocols must have the reduction in energy consumption as a major goal. This can be done using numerous strategies. Such strategies heavily depend on the application that is involved, the level of responsiveness and readiness of the robots, as well as the nature of the tasks that they performed.
- Network throughput: Different applications and the related services and tasks performed by the robots and the MRS network require varied amounts of communication bandwidth. Depending on the network conditions and the amount of traffic that is being exchanged, there might be dropped messages due to congestion, collisions, interference, and delay. As a result, the throughput of the network might vary considerably leading to problems with applications that require higher data rates and can tolerate only a limited amount of delay. Consequently, the network protocol needs to ensure that the specific throughput that is needed by the applications is satisfied by the underlying network.
- Co-located networking protocols: In addition to the routing protocol that is used to route messages between robots (R2R link) in an MRS system at the network layer, it is highly likely to have other wireless protocols that are used by R2I links or other wireless networks that are in the same area. Consequently, it is important to take that into consideration when choosing the right protocol to reduce the possibility of interference when the protocols operate in the same frequency range. This is especially important in MRS systems that perform critical missions where errors can cause life-threatening or catastrophic results.
- Connection to backbone: In the cases where the MRS network needs to be connected to a backbone network or to the Internet, there is a need to map the QoS and other networking parameters of the R2R and R2I traffic from the header of the MRS packet to the corresponding parameters in the headers of the upper layers in the backbone network. This should be done in a way that ensures seamless integration of the two systems, which does not lead to appreciable changes in the QoS of the associated MRS traffic.
- Mobility: Nodes in an MRS network are typically mobile. However, the mobility degree and pattern can vary widely between different applications. For example, MRS networks with nodes consisting of UAVs are expected to move at considerably higher speeds than nodes in an MRS with nodes consisting of robots moving on rough terrain in a search-and-rescue mission. Consequently, the routing protocol that is chosen for the MRS network must be characterized by higher efficiency for the mobility degree and pattern of the particular MRS network.
- Handoff and roaming: MRS networks with a very large number of nodes (hundreds or thousands) typically need to have multiple layers of hierarchy as indicated earlier. Robots in a particular cluster usually transmit their data to an elected CH or a gateway node that provides connection to the rest of the robots in the MRS or a backbone network. However, when a particular robot goes out of range of its designated or current gateway and into the range of another gateway, a handoff process needs to take place. When robots require changing their connection from a particular network to another one due to geographic movement a roaming mechanism must take place (for example, the MRS might be using cellular connections). Such considerations are important to keep in mind at the network design and configuration phases.
- Security: Depending on the nature, confidentially, and criticality of the MRS application, secure communication might be an essential requirement to ensure its success. Certain MRS networks that perform military, or sensitive commercial services might be subject to many kinds of attacks. The attacks can be internal or external to the network. Consequently, the communication protocol that is chosen must have security provisioning to ensure protection against possible attacks. Such attacks can be passive or active. Passive attacks include eavesdropping, and traffic analysis. Active attacks include masquerade, replay, modification of message content, and denial or service (DoS).
- Reliability: To varying degrees of importance, reliability of the communication process is an important aspect of certain MRS networks. For certain types of applications, R2R and R2I communication cannot tolerate interruptions, which might lead to mission disruption, or failure, which in turn can result in high financial losses or cause life-threatening conditions. Subsequently, the MRS network protocol need to include reliability features such as fail-safe provisioning, redundancy of critical communication components, or designated nodes to replace other communication-critical nodes in case of malfunctioning or failure.
4.3. Robot-to-Infrastructure (R2I) Communication
4.4. Communication Links in MRS Systems
4.5. The Networking Layers in the Nodes of the MRS System
4.6. MRS Middleware Layer Functions, Services, and Requirements
- R2R communication.
- Robots acting as relay nodes.
- Robots acting as gateway nodes (R2I links).
- Robots assisting in sensing operations.
- Robot providing data storage services.
- Robots providing processing services.
- Implementation of distributed or centralized control of robot teams.
4.7. Types of Traffic Generated by MRS Systems and Associated Requirements
4.8. Robot Operating System Networking Services
5. Networking for Cloud and Fog-Based Multi-Robot Systems
5.1. Cloud-Based Multi-Robot Systems
5.2. Fog-Based Multi-Robot Systems
Conflicts of Interest
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|Protocol||Main Characteristics||Physical Layer Specs||Data Link Layer Specs||Data Rate||Transmission Range||MRS System Communication and Link Types|
|IEEE 802.15.1 (Bluetooth)||Low- to medium-rate short-range communication||2.4 GHz band, FHSS/FSK||Master/Slave, Piconet/Scatternet architecture||1 to 24 Mbps||10 m||Short-range R2R and R2I communication (e.g., task synchronization, small-size telemetric data exchange, etc.)|
|IEEE 802.15.3||High rate cable replacement||2–9 GHz and 57–66 GHz range bands, OFDM, direct sequence UWB||Master/Slave, Piconet/Scatternet architecture||11–55 Mbps||10 m||High-rate short-range R2R and R2I communication (e.g., audio/video data exchange, etc.)|
|IEEE 802.11a/b/g/n/ac||Local Area Network, medium range||2.4 GHz and 5 GHz band, DSSS, OFDM||CSMA/CA, DFS/PFS Mechanisms||15, 30, 45, 60, 90, 120, 135, 150, 346, 800, 3466 Mbps||up to 250 m outdoors||R2R and R2I links with medium range communication and medium to high data rate.|
|IEEE 802.16/rel 1/rel 1.5/rel 2(m) (WiMAX)||Metropolitan Area Network||2 to 66 GHz band, MIMO-OFDMA||TDD, FDD||2 to 75 Mbps||Up to 35 miles (56 Km)||R2R and R2I links with high data rates and longer range. Supports various types of back haul data traffic.|
|Cellular 3G||Long range. Packet switched data. Voice support with packet or circuit switched connectivity.||800 MHz to 1900 MHz||CDMA, HSDPA||144 Kbps (mobile users) to 42 Mbps (for stationary users)||Cell radius dependent, up to several Km’s||Mostly R2I links to provide connectivity to infrastructure networks and the Internet in areas where other wireless services are not available.|
|Cellular 4G/LTE||same as 3G||700 MHz to 2500 MHz||LTE and LTE Advanced||300 Mbps to 1 Gbps||Cell radius dependent, up to several Km’s||Mostly R2I links with higher data rate requirements to provide connectivity to infrastructure networks and the Internet in areas where other wireless services are not available.|
|Satellite (LEO/MEO/GEO)||Wide Area Network||1.53 GHz to 31 GHz||FDMA and TDMA||10 Mbps (upload) and 1 Gbps (download)||Covers hundreds of Km’s to entire earth||R2I links supporting data traffic communication to infrastructure networks and the Internet in remote areas where other connectivity is not readily available.|
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Jawhar, I.; Mohamed, N.; Wu, J.; Al-Jaroodi, J. Networking of Multi-Robot Systems: Architectures and Requirements. J. Sens. Actuator Netw. 2018, 7, 52. https://doi.org/10.3390/jsan7040052
Jawhar I, Mohamed N, Wu J, Al-Jaroodi J. Networking of Multi-Robot Systems: Architectures and Requirements. Journal of Sensor and Actuator Networks. 2018; 7(4):52. https://doi.org/10.3390/jsan7040052Chicago/Turabian Style
Jawhar, Imad, Nader Mohamed, Jie Wu, and Jameela Al-Jaroodi. 2018. "Networking of Multi-Robot Systems: Architectures and Requirements" Journal of Sensor and Actuator Networks 7, no. 4: 52. https://doi.org/10.3390/jsan7040052